Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x1bc9e135630>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x1bc9e30cba8>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests
import importlib
importlib.reload(tests)

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    inputs_real = tf.placeholder(tf.float32, [None, image_width, image_height, image_channels], name='input_real')
    inputs_z = tf.placeholder(tf.float32, [None, z_dim], name='input_z')
    learning_rate = tf.placeholder(tf.float32, name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [6]:
def lrelu(tensor, alpha=0.2):
    """
    Leaky ReLU implementation
    :param tensor: Input Tensor
    :param alpha: Leak scaling for negative inputs
    :return: Leaky ReLU output
    """
    return tf.maximum(tensor * alpha, tensor)

def discriminator(images, reuse=False, alpha=0.2, training=True):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    Optional
    :param alpha: Leak factor for Leaky ReLU
    :param training: Boolean for batch normalization to use batch or population statistics
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    # input is 28x28x1 or 28x28x3
    with tf.variable_scope('discriminator', reuse=reuse):
        x = tf.layers.conv2d(images, filters=64, kernel_size=5, strides=2, padding='same', activation=None)
        x = lrelu(x, alpha)
        # Now 14x14x64
        
        # 2 conv layers for 7x7x128 -> 4x4x256
        for filter_depth in [128, 256]:
            x = tf.layers.conv2d(x, filters=filter_depth, kernel_size=5, strides=2, padding='same', activation=None, use_bias=False)
            # Use batch normalization
            x = tf.layers.batch_normalization(x, training=training)
            x = lrelu(x, alpha)
        # Now 4x4x256
        
        flat = tf.reshape(x, [-1, 4*4*256])
        logits = tf.layers.dense(flat, 1, activation=None)
        out = tf.sigmoid(logits)
        #print(x.shape, flat.shape, logits.shape)

    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    Optional
    :param alpha: Leak factor for Leaky ReLU
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    # during training, don't reuse, during generation, reuse variables
    with tf.variable_scope('generator', reuse=not is_train):
        # Fully connected layer
        x = tf.layers.dense(z, 2*2*1024, activation=None, use_bias=False)
        # Reshape to start the conv stack
        x = tf.reshape(x, (-1, 2, 2, 1024))
        x = lrelu(x, alpha)
        # Now 2x2x1024
        
        x = tf.layers.conv2d_transpose(x, 512, kernel_size=5, strides=2, padding='valid', activation=None, use_bias=False)
        x = tf.layers.batch_normalization(x, training=is_train)
        x = lrelu(x, alpha)
        # 7x7x512
        
        #print(x.shape)
        x = tf.layers.conv2d_transpose(x, 256, kernel_size=5, strides=2, padding='same', activation=None, use_bias=False)
        x = tf.layers.batch_normalization(x, training=is_train)
        x = lrelu(x, alpha)
        # 14x14x256
        
        #print(x.shape)
        logits = tf.layers.conv2d_transpose(x, out_channel_dim, kernel_size=5, strides=2, padding='same')
        # 28x28xout_channel_dim
        
        #print(logits.shape)
        # the real images from get_batches are scaled between -0.5 and 0.5
        #scale = 0.5
        #out = tf.multiply(tf.tanh(logits), scale)
        out = tf.tanh(logits)
    
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2, label_smooth=0.1):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    Optional
    :param alpha: Leak factor for Leaky ReLU
    :param label_smooth: Label smoothing for better discriminator generalization
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, alpha=alpha)
    d_model_real, d_logits_real = discriminator(input_real, alpha=alpha)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha)
    
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - label_smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # Get batch normalization variables to update
    d_update_opts = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='discriminator')
    g_update_opts = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')
    
    with tf.control_dependencies(d_update_opts):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    with tf.control_dependencies(g_update_opts):
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
try:
    from tqdm import tqdm
except ImportError:
    def tqdm(x):
        return x
In [12]:
!mkdir checkpoints
A subdirectory or file checkpoints already exists.
In [13]:
def plot_losses(losses):
    """
    Plot training losses
    :param losses: list of tuples of (discriminator loss, generator loss)
    """
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    # each row in losses is (discriminator loss, generator loss)
    losses = np.array(losses)
    # transpose of losses would give all discriminator losses as first row and generator losses as second row
    plt.plot(losses.T[0], label='Discriminator', alpha=0.3)
    plt.plot(losses.T[1], label='Generator')
    plt.title('Training Losses')
    plt.legend()
In [14]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, alpha=0.2, 
          print_every=10, show_every=100, show_n_images=25):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    Optional
    :param alpha: Leak factor for Leaky ReLU
    :param print_every: Print loss every n steps
    :param show_every: Show images generated every n steps
    :param show_n_images: Number of images to show each time
    """
    # TODO: Build Model
    
    # data_shape is (samples x width x height x channels)
    input_real, input_z, learn_rate = model_inputs(*data_shape[1:], z_dim)
    out_channel_dim = data_shape[-1]
    d_loss, g_loss = model_loss(input_real, input_z, out_channel_dim, alpha=alpha)
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    saver = tf.train.Saver()
    steps = 0
    losses = []
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        epoch_pbar = tqdm(range(epoch_count), desc='Epochs', unit='epoch')
        
        #for epoch_i in range(epoch_count):
        for epoch_i in epoch_pbar:
            batch_count = data_shape[0]//batch_size
            batch_pbar = tqdm(get_batches(batch_size), desc='Batches', unit='batch', miniters=show_every, 
                              total=batch_count, maxinterval=360)
            batch_i = 0
            #for batch_images in get_batches(batch_size):
            for batch_images in batch_pbar:
                # TODO: Train Model
                steps += 1
                batch_i += 1
                # the real images from get_batches are scaled between -0.5 and 0.5, so we scale to -1 to 1 to match generator
                batch_images *= 2
                # Sample random noise for Generator
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                # Run optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                # Train generator twice to meet loss lower than discriminator requirement
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, learn_rate: learning_rate})
                
                if steps % print_every == 0:
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print('Epoch {}/{}...'.format(epoch_i, epoch_count),
                          'Batch {}/{}...'.format(batch_i, batch_count),
                          'Discriminator loss: {:.4f}'.format(train_loss_d),
                          'Generator loss: {:.4f}'.format(train_loss_g))
                    losses.append((train_loss_d, train_loss_g))
                
                if steps % show_every == 0:
                    show_generator_output(sess, show_n_images, input_z, out_channel_dim, data_image_mode)
        saver.save(sess, './checkpoints/generator_bs{}_zd{}_lr{}_b{}.ckpt'.format(batch_size, z_dim, learning_rate, beta1))
    plot_losses(losses)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [15]:
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
mnist_dataset.shape
Out[15]:
(60000, 28, 28, 1)
In [16]:
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
celeba_dataset.shape
Out[16]:
(202599, 28, 28, 3)
In [17]:
batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/2 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                    | 0/468 [00:00<?, ?batch/s]
Epoch 0/2... Batch 10/468... Discriminator loss: 0.4917 Generator loss: 5.2180
Epoch 0/2... Batch 20/468... Discriminator loss: 3.6643 Generator loss: 0.0700
Epoch 0/2... Batch 30/468... Discriminator loss: 2.1145 Generator loss: 3.2754
Epoch 0/2... Batch 40/468... Discriminator loss: 1.9406 Generator loss: 1.6231
Epoch 0/2... Batch 50/468... Discriminator loss: 0.8335 Generator loss: 1.9555
Epoch 0/2... Batch 60/468... Discriminator loss: 1.3331 Generator loss: 0.9221
Epoch 0/2... Batch 70/468... Discriminator loss: 1.4760 Generator loss: 0.5973
Epoch 0/2... Batch 80/468... Discriminator loss: 0.9745 Generator loss: 2.3600
Epoch 0/2... Batch 90/468... Discriminator loss: 0.8449 Generator loss: 1.4911
Epoch 0/2... Batch 100/468... Discriminator loss: 0.8251 Generator loss: 1.5158
Batches:  21%|███████████████▊                                                          | 100/468 [00:26<01:38,  3.73batch/s]
Epoch 0/2... Batch 110/468... Discriminator loss: 1.2290 Generator loss: 1.7586
Epoch 0/2... Batch 120/468... Discriminator loss: 1.5158 Generator loss: 0.6855
Epoch 0/2... Batch 130/468... Discriminator loss: 1.6074 Generator loss: 0.9875
Epoch 0/2... Batch 140/468... Discriminator loss: 2.0240 Generator loss: 0.3558
Epoch 0/2... Batch 150/468... Discriminator loss: 0.9545 Generator loss: 1.3825
Epoch 0/2... Batch 160/468... Discriminator loss: 1.1279 Generator loss: 1.2117
Epoch 0/2... Batch 170/468... Discriminator loss: 1.1458 Generator loss: 1.0389
Epoch 0/2... Batch 180/468... Discriminator loss: 1.1388 Generator loss: 1.1487
Epoch 0/2... Batch 190/468... Discriminator loss: 1.2500 Generator loss: 1.3857
Epoch 0/2... Batch 200/468... Discriminator loss: 1.7996 Generator loss: 0.7772
Batches:  43%|███████████████████████████████▌                                          | 200/468 [00:52<01:10,  3.80batch/s]
Epoch 0/2... Batch 210/468... Discriminator loss: 1.2502 Generator loss: 0.8111
Epoch 0/2... Batch 220/468... Discriminator loss: 1.1672 Generator loss: 1.0612
Epoch 0/2... Batch 230/468... Discriminator loss: 1.4713 Generator loss: 0.8002
Epoch 0/2... Batch 240/468... Discriminator loss: 1.1337 Generator loss: 1.2037
Epoch 0/2... Batch 250/468... Discriminator loss: 1.2027 Generator loss: 1.0628
Epoch 0/2... Batch 260/468... Discriminator loss: 1.2021 Generator loss: 1.3674
Epoch 0/2... Batch 270/468... Discriminator loss: 1.4274 Generator loss: 0.6277
Epoch 0/2... Batch 280/468... Discriminator loss: 1.2764 Generator loss: 1.0985
Epoch 0/2... Batch 290/468... Discriminator loss: 1.1557 Generator loss: 1.0927
Epoch 0/2... Batch 300/468... Discriminator loss: 1.2954 Generator loss: 0.8428
Batches:  64%|███████████████████████████████████████████████▍                          | 300/468 [01:19<00:44,  3.75batch/s]
Epoch 0/2... Batch 310/468... Discriminator loss: 1.3570 Generator loss: 1.0544
Epoch 0/2... Batch 320/468... Discriminator loss: 1.3732 Generator loss: 1.0085
Epoch 0/2... Batch 330/468... Discriminator loss: 1.3976 Generator loss: 1.1268
Epoch 0/2... Batch 340/468... Discriminator loss: 1.3836 Generator loss: 1.1496
Epoch 0/2... Batch 350/468... Discriminator loss: 1.2577 Generator loss: 1.3831
Epoch 0/2... Batch 360/468... Discriminator loss: 1.4038 Generator loss: 0.8038
Epoch 0/2... Batch 370/468... Discriminator loss: 1.4000 Generator loss: 1.0657
Epoch 0/2... Batch 380/468... Discriminator loss: 1.2514 Generator loss: 0.8665
Epoch 0/2... Batch 390/468... Discriminator loss: 1.4491 Generator loss: 0.6437
Epoch 0/2... Batch 400/468... Discriminator loss: 1.2428 Generator loss: 0.9599
Batches:  85%|███████████████████████████████████████████████████████████████▏          | 400/468 [01:44<00:17,  3.82batch/s]
Epoch 0/2... Batch 410/468... Discriminator loss: 1.4123 Generator loss: 1.3603
Epoch 0/2... Batch 420/468... Discriminator loss: 1.4848 Generator loss: 0.4702
Epoch 0/2... Batch 430/468... Discriminator loss: 1.3709 Generator loss: 0.9749
Epoch 0/2... Batch 440/468... Discriminator loss: 1.3116 Generator loss: 1.0834
Epoch 0/2... Batch 450/468... Discriminator loss: 1.2400 Generator loss: 1.0020
Epoch 0/2... Batch 460/468... Discriminator loss: 1.2240 Generator loss: 1.1380
Epochs:  50%|███████████████████████████████████████                                       | 1/2 [02:01<02:01, 121.09s/epoch]
Batches:   0%|                                                                                    | 0/468 [00:00<?, ?batch/s]
Epoch 1/2... Batch 2/468... Discriminator loss: 1.3871 Generator loss: 0.7944
Epoch 1/2... Batch 12/468... Discriminator loss: 1.2662 Generator loss: 0.7959
Epoch 1/2... Batch 22/468... Discriminator loss: 1.3655 Generator loss: 0.6302
Epoch 1/2... Batch 32/468... Discriminator loss: 1.4927 Generator loss: 0.9913
Epoch 1/2... Batch 42/468... Discriminator loss: 1.2931 Generator loss: 0.7734
Epoch 1/2... Batch 52/468... Discriminator loss: 1.3677 Generator loss: 0.6417
Epoch 1/2... Batch 62/468... Discriminator loss: 1.4126 Generator loss: 0.6299
Epoch 1/2... Batch 72/468... Discriminator loss: 1.3792 Generator loss: 0.6583
Epoch 1/2... Batch 82/468... Discriminator loss: 1.3868 Generator loss: 0.8547
Epoch 1/2... Batch 92/468... Discriminator loss: 1.2090 Generator loss: 0.9803
Batches:  21%|███████████████▊                                                          | 100/468 [00:24<01:31,  4.02batch/s]
Epoch 1/2... Batch 102/468... Discriminator loss: 1.3643 Generator loss: 0.9544
Epoch 1/2... Batch 112/468... Discriminator loss: 1.3566 Generator loss: 0.9705
Epoch 1/2... Batch 122/468... Discriminator loss: 1.3685 Generator loss: 0.8042
Epoch 1/2... Batch 132/468... Discriminator loss: 1.2974 Generator loss: 1.1905
Epoch 1/2... Batch 142/468... Discriminator loss: 1.3170 Generator loss: 0.6889
Epoch 1/2... Batch 152/468... Discriminator loss: 1.2391 Generator loss: 0.8194
Epoch 1/2... Batch 162/468... Discriminator loss: 1.4350 Generator loss: 0.9195
Epoch 1/2... Batch 172/468... Discriminator loss: 1.4016 Generator loss: 0.6460
Epoch 1/2... Batch 182/468... Discriminator loss: 1.3350 Generator loss: 0.6979
Epoch 1/2... Batch 192/468... Discriminator loss: 1.4232 Generator loss: 0.8011
Batches:  43%|███████████████████████████████▌                                          | 200/468 [00:49<01:06,  4.04batch/s]
Epoch 1/2... Batch 202/468... Discriminator loss: 1.5059 Generator loss: 1.2567
Epoch 1/2... Batch 212/468... Discriminator loss: 1.3714 Generator loss: 0.9867
Epoch 1/2... Batch 222/468... Discriminator loss: 1.3557 Generator loss: 1.4190
Epoch 1/2... Batch 232/468... Discriminator loss: 1.3524 Generator loss: 0.9505
Epoch 1/2... Batch 242/468... Discriminator loss: 1.4540 Generator loss: 0.6657
Epoch 1/2... Batch 252/468... Discriminator loss: 1.3012 Generator loss: 1.1714
Epoch 1/2... Batch 262/468... Discriminator loss: 1.4659 Generator loss: 0.8659
Epoch 1/2... Batch 272/468... Discriminator loss: 1.3775 Generator loss: 1.1010
Epoch 1/2... Batch 282/468... Discriminator loss: 1.2365 Generator loss: 0.8836
Epoch 1/2... Batch 292/468... Discriminator loss: 1.3333 Generator loss: 0.8778
Batches:  64%|███████████████████████████████████████████████▍                          | 300/468 [01:14<00:41,  4.03batch/s]
Epoch 1/2... Batch 302/468... Discriminator loss: 1.2817 Generator loss: 1.0011
Epoch 1/2... Batch 312/468... Discriminator loss: 1.4207 Generator loss: 1.0578
Epoch 1/2... Batch 322/468... Discriminator loss: 1.4541 Generator loss: 0.5665
Epoch 1/2... Batch 332/468... Discriminator loss: 1.3423 Generator loss: 0.7892
Epoch 1/2... Batch 342/468... Discriminator loss: 1.3868 Generator loss: 0.6084
Epoch 1/2... Batch 352/468... Discriminator loss: 1.3785 Generator loss: 1.1364
Epoch 1/2... Batch 362/468... Discriminator loss: 1.3157 Generator loss: 0.7466
Epoch 1/2... Batch 372/468... Discriminator loss: 1.4727 Generator loss: 1.4169
Epoch 1/2... Batch 382/468... Discriminator loss: 1.4244 Generator loss: 0.7485
Epoch 1/2... Batch 392/468... Discriminator loss: 1.3714 Generator loss: 0.9000
Batches:  85%|███████████████████████████████████████████████████████████████▏          | 400/468 [01:40<00:17,  3.96batch/s]
Epoch 1/2... Batch 402/468... Discriminator loss: 1.4589 Generator loss: 1.1114
Epoch 1/2... Batch 412/468... Discriminator loss: 1.3919 Generator loss: 1.0841
Epoch 1/2... Batch 422/468... Discriminator loss: 1.4290 Generator loss: 0.5822
Epoch 1/2... Batch 432/468... Discriminator loss: 1.4165 Generator loss: 1.2199
Epoch 1/2... Batch 442/468... Discriminator loss: 1.3519 Generator loss: 0.7808
Epoch 1/2... Batch 452/468... Discriminator loss: 1.3349 Generator loss: 0.8850
Epoch 1/2... Batch 462/468... Discriminator loss: 1.3478 Generator loss: 0.7168
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 2/2 [04:00<00:00, 120.68s/epoch]
In [17]:
batch_size = 128
z_dim = 200
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/2 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                    | 0/468 [00:00<?, ?batch/s]
Epoch 0/2... Batch 10/468... Discriminator loss: 0.4435 Generator loss: 10.7345
Epoch 0/2... Batch 20/468... Discriminator loss: 0.6819 Generator loss: 6.3631
Epoch 0/2... Batch 30/468... Discriminator loss: 2.0784 Generator loss: 0.2784
Epoch 0/2... Batch 40/468... Discriminator loss: 1.5113 Generator loss: 0.5470
Epoch 0/2... Batch 50/468... Discriminator loss: 1.9211 Generator loss: 2.1227
Epoch 0/2... Batch 60/468... Discriminator loss: 1.6089 Generator loss: 1.6311
Epoch 0/2... Batch 70/468... Discriminator loss: 1.1816 Generator loss: 2.3494
Epoch 0/2... Batch 80/468... Discriminator loss: 0.7240 Generator loss: 2.5696
Epoch 0/2... Batch 90/468... Discriminator loss: 1.6865 Generator loss: 2.2101
Epoch 0/2... Batch 100/468... Discriminator loss: 1.0282 Generator loss: 2.3086
Batches:  21%|███████████████▊                                                          | 100/468 [00:50<03:05,  1.98batch/s]
Epoch 0/2... Batch 110/468... Discriminator loss: 1.0414 Generator loss: 2.3266
Epoch 0/2... Batch 120/468... Discriminator loss: 0.8370 Generator loss: 1.2102
Epoch 0/2... Batch 130/468... Discriminator loss: 0.8832 Generator loss: 1.4083
Epoch 0/2... Batch 140/468... Discriminator loss: 1.0062 Generator loss: 3.5232
Epoch 0/2... Batch 150/468... Discriminator loss: 1.4988 Generator loss: 0.5457
Epoch 0/2... Batch 160/468... Discriminator loss: 1.2900 Generator loss: 1.2644
Epoch 0/2... Batch 170/468... Discriminator loss: 0.8047 Generator loss: 1.4298
Epoch 0/2... Batch 180/468... Discriminator loss: 1.2491 Generator loss: 0.8524
Epoch 0/2... Batch 190/468... Discriminator loss: 1.5369 Generator loss: 3.5946
Epoch 0/2... Batch 200/468... Discriminator loss: 1.2011 Generator loss: 1.0701
Batches:  43%|███████████████████████████████▌                                          | 200/468 [01:36<02:11,  2.03batch/s]
Epoch 0/2... Batch 210/468... Discriminator loss: 1.1852 Generator loss: 0.9097
Epoch 0/2... Batch 220/468... Discriminator loss: 1.2709 Generator loss: 1.9000
Epoch 0/2... Batch 230/468... Discriminator loss: 1.5873 Generator loss: 0.6840
Epoch 0/2... Batch 240/468... Discriminator loss: 0.9993 Generator loss: 1.4596
Epoch 0/2... Batch 250/468... Discriminator loss: 1.2687 Generator loss: 1.4305
Epoch 0/2... Batch 260/468... Discriminator loss: 1.1471 Generator loss: 1.2122
Epoch 0/2... Batch 270/468... Discriminator loss: 1.0257 Generator loss: 1.2598
Epoch 0/2... Batch 280/468... Discriminator loss: 1.0547 Generator loss: 1.1313
Epoch 0/2... Batch 290/468... Discriminator loss: 1.3830 Generator loss: 0.6868
Epoch 0/2... Batch 300/468... Discriminator loss: 0.9425 Generator loss: 1.2102
Batches:  64%|███████████████████████████████████████████████▍                          | 300/468 [02:24<01:21,  2.05batch/s]
Epoch 0/2... Batch 310/468... Discriminator loss: 1.3713 Generator loss: 1.1332
Epoch 0/2... Batch 320/468... Discriminator loss: 1.1907 Generator loss: 1.0091
Epoch 0/2... Batch 330/468... Discriminator loss: 1.4063 Generator loss: 1.2298
Epoch 0/2... Batch 340/468... Discriminator loss: 1.6128 Generator loss: 1.0067
Epoch 0/2... Batch 350/468... Discriminator loss: 1.5502 Generator loss: 0.8953
Epoch 0/2... Batch 360/468... Discriminator loss: 1.4479 Generator loss: 1.0112
Epoch 0/2... Batch 370/468... Discriminator loss: 1.1682 Generator loss: 1.5175
Epoch 0/2... Batch 380/468... Discriminator loss: 1.3367 Generator loss: 0.8333
Epoch 0/2... Batch 390/468... Discriminator loss: 1.3867 Generator loss: 1.1652
Epoch 0/2... Batch 400/468... Discriminator loss: 1.3632 Generator loss: 1.0347
Batches:  85%|███████████████████████████████████████████████████████████████▏          | 400/468 [03:13<00:33,  2.05batch/s]
Epoch 0/2... Batch 410/468... Discriminator loss: 1.3535 Generator loss: 1.2404
Epoch 0/2... Batch 420/468... Discriminator loss: 1.7433 Generator loss: 0.3725
Epoch 0/2... Batch 430/468... Discriminator loss: 1.4140 Generator loss: 1.2692
Epoch 0/2... Batch 440/468... Discriminator loss: 1.3175 Generator loss: 0.8893
Epoch 0/2... Batch 450/468... Discriminator loss: 1.3028 Generator loss: 0.9682
Epoch 0/2... Batch 460/468... Discriminator loss: 1.2309 Generator loss: 1.0571
Epochs:  50%|███████████████████████████████████████                                       | 1/2 [03:46<03:46, 226.36s/epoch]
Batches:   0%|                                                                                    | 0/468 [00:00<?, ?batch/s]
Epoch 1/2... Batch 2/468... Discriminator loss: 1.3587 Generator loss: 0.6758
Epoch 1/2... Batch 12/468... Discriminator loss: 1.2621 Generator loss: 0.8704
Epoch 1/2... Batch 22/468... Discriminator loss: 1.2539 Generator loss: 1.0465
Epoch 1/2... Batch 32/468... Discriminator loss: 1.3647 Generator loss: 0.6363
Epoch 1/2... Batch 42/468... Discriminator loss: 1.3266 Generator loss: 0.7088
Epoch 1/2... Batch 52/468... Discriminator loss: 1.4700 Generator loss: 0.5024
Epoch 1/2... Batch 62/468... Discriminator loss: 1.3791 Generator loss: 0.9320
Epoch 1/2... Batch 72/468... Discriminator loss: 1.2789 Generator loss: 0.7328
Epoch 1/2... Batch 82/468... Discriminator loss: 1.4500 Generator loss: 0.8968
Epoch 1/2... Batch 92/468... Discriminator loss: 1.2438 Generator loss: 0.9133
Batches:  21%|███████████████▊                                                          | 100/468 [00:25<01:32,  3.97batch/s]
Epoch 1/2... Batch 102/468... Discriminator loss: 1.3111 Generator loss: 1.0705
Epoch 1/2... Batch 112/468... Discriminator loss: 1.3884 Generator loss: 1.3091
Epoch 1/2... Batch 122/468... Discriminator loss: 1.4374 Generator loss: 0.7862
Epoch 1/2... Batch 132/468... Discriminator loss: 1.6408 Generator loss: 0.4247
Epoch 1/2... Batch 142/468... Discriminator loss: 1.3992 Generator loss: 1.1587
Epoch 1/2... Batch 152/468... Discriminator loss: 1.2317 Generator loss: 0.8792
Epoch 1/2... Batch 162/468... Discriminator loss: 1.3237 Generator loss: 0.8866
Epoch 1/2... Batch 172/468... Discriminator loss: 1.3876 Generator loss: 0.6790
Epoch 1/2... Batch 182/468... Discriminator loss: 1.4227 Generator loss: 1.0679
Epoch 1/2... Batch 192/468... Discriminator loss: 1.4026 Generator loss: 0.6883
Batches:  43%|███████████████████████████████▌                                          | 200/468 [00:49<01:07,  3.99batch/s]
Epoch 1/2... Batch 202/468... Discriminator loss: 1.3960 Generator loss: 1.0398
Epoch 1/2... Batch 212/468... Discriminator loss: 1.3786 Generator loss: 0.7405
Epoch 1/2... Batch 222/468... Discriminator loss: 1.2789 Generator loss: 0.7547
Epoch 1/2... Batch 232/468... Discriminator loss: 1.3206 Generator loss: 0.7819
Epoch 1/2... Batch 242/468... Discriminator loss: 1.4169 Generator loss: 0.6331
Epoch 1/2... Batch 252/468... Discriminator loss: 1.2402 Generator loss: 1.0589
Epoch 1/2... Batch 262/468... Discriminator loss: 1.3947 Generator loss: 0.9656
Epoch 1/2... Batch 272/468... Discriminator loss: 1.2891 Generator loss: 1.2145
Epoch 1/2... Batch 282/468... Discriminator loss: 1.1983 Generator loss: 0.9905
Epoch 1/2... Batch 292/468... Discriminator loss: 1.3305 Generator loss: 0.9516
Batches:  64%|███████████████████████████████████████████████▍                          | 300/468 [01:15<00:42,  3.99batch/s]
Epoch 1/2... Batch 302/468... Discriminator loss: 1.2572 Generator loss: 0.7917
Epoch 1/2... Batch 312/468... Discriminator loss: 1.4530 Generator loss: 0.5764
Epoch 1/2... Batch 322/468... Discriminator loss: 1.4206 Generator loss: 0.6058
Epoch 1/2... Batch 332/468... Discriminator loss: 1.3564 Generator loss: 0.8770
Epoch 1/2... Batch 342/468... Discriminator loss: 1.3475 Generator loss: 0.7495
Epoch 1/2... Batch 352/468... Discriminator loss: 1.5035 Generator loss: 1.3818
Epoch 1/2... Batch 362/468... Discriminator loss: 1.3239 Generator loss: 0.9159
Epoch 1/2... Batch 372/468... Discriminator loss: 1.4482 Generator loss: 1.1449
Epoch 1/2... Batch 382/468... Discriminator loss: 1.3423 Generator loss: 0.8676
Epoch 1/2... Batch 392/468... Discriminator loss: 1.4075 Generator loss: 0.8432
Batches:  85%|███████████████████████████████████████████████████████████████▏          | 400/468 [01:40<00:17,  3.99batch/s]
Epoch 1/2... Batch 402/468... Discriminator loss: 1.4808 Generator loss: 1.1490
Epoch 1/2... Batch 412/468... Discriminator loss: 1.3945 Generator loss: 1.1055
Epoch 1/2... Batch 422/468... Discriminator loss: 1.6173 Generator loss: 0.4040
Epoch 1/2... Batch 432/468... Discriminator loss: 1.3005 Generator loss: 0.7706
Epoch 1/2... Batch 442/468... Discriminator loss: 1.4244 Generator loss: 0.6941
Epoch 1/2... Batch 452/468... Discriminator loss: 1.2694 Generator loss: 0.8146
Epoch 1/2... Batch 462/468... Discriminator loss: 1.3219 Generator loss: 0.7451
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 2/2 [05:43<00:00, 193.61s/epoch]

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2
# Generator Losses seem ok, but faces generated are bad

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [ ]:
batch_size = 64
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2
# Generator Losses seem ok, but faces generated are bad

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2
# Generator Losses seem ok, but faces generated are bad

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [ ]:
batch_size = 32
z_dim = 200 # Try doubling from earlier attempt of 100
learning_rate = 0.0005
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [18]:
batch_size = 256
z_dim = 200
learning_rate = 0.01 # too high
beta1 = 0.5
alpha = 0.2
# Generator Losses jumping around too much. Batch size and learning rate seem too high to be stable. Faces are too noisy

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                    | 0/791 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/791... Discriminator loss: 3.4570 Generator loss: 0.1437
Epoch 0/1... Batch 20/791... Discriminator loss: 2.4284 Generator loss: 3.3371
Epoch 0/1... Batch 30/791... Discriminator loss: 0.6787 Generator loss: 5.0026
Epoch 0/1... Batch 40/791... Discriminator loss: 1.2642 Generator loss: 6.0321
Epoch 0/1... Batch 50/791... Discriminator loss: 2.6873 Generator loss: 1.6424
Epoch 0/1... Batch 60/791... Discriminator loss: 0.6704 Generator loss: 2.7082
Epoch 0/1... Batch 70/791... Discriminator loss: 1.0521 Generator loss: 2.3508
Epoch 0/1... Batch 80/791... Discriminator loss: 1.3881 Generator loss: 1.0394
Epoch 0/1... Batch 90/791... Discriminator loss: 0.6054 Generator loss: 2.5848
Epoch 0/1... Batch 100/791... Discriminator loss: 0.5554 Generator loss: 1.8850
Batches:  13%|█████████▎                                                                | 100/791 [02:05<14:24,  1.25s/batch]
Epoch 0/1... Batch 110/791... Discriminator loss: 0.4814 Generator loss: 3.1759
Epoch 0/1... Batch 120/791... Discriminator loss: 0.3945 Generator loss: 4.0385
Epoch 0/1... Batch 130/791... Discriminator loss: 0.9977 Generator loss: 1.0179
Epoch 0/1... Batch 140/791... Discriminator loss: 1.0327 Generator loss: 1.8289
Epoch 0/1... Batch 150/791... Discriminator loss: 1.0169 Generator loss: 1.8406
Epoch 0/1... Batch 160/791... Discriminator loss: 0.6310 Generator loss: 2.2822
Epoch 0/1... Batch 170/791... Discriminator loss: 1.0231 Generator loss: 1.4359
Epoch 0/1... Batch 180/791... Discriminator loss: 0.7165 Generator loss: 1.8134
Epoch 0/1... Batch 190/791... Discriminator loss: 0.4087 Generator loss: 3.2192
Epoch 0/1... Batch 200/791... Discriminator loss: 0.3802 Generator loss: 3.9742
Batches:  25%|██████████████████▋                                                       | 200/791 [04:05<12:10,  1.24s/batch]
Epoch 0/1... Batch 210/791... Discriminator loss: 0.3910 Generator loss: 3.1805
Epoch 0/1... Batch 220/791... Discriminator loss: 0.3506 Generator loss: 4.3192
Epoch 0/1... Batch 230/791... Discriminator loss: 0.4349 Generator loss: 4.6078
Epoch 0/1... Batch 240/791... Discriminator loss: 0.3470 Generator loss: 4.2532
Epoch 0/1... Batch 250/791... Discriminator loss: 0.3636 Generator loss: 3.7173
Epoch 0/1... Batch 260/791... Discriminator loss: 0.3403 Generator loss: 4.6121
Epoch 0/1... Batch 270/791... Discriminator loss: 0.3348 Generator loss: 5.3991
Epoch 0/1... Batch 280/791... Discriminator loss: 0.3412 Generator loss: 5.8729
Epoch 0/1... Batch 290/791... Discriminator loss: 0.3323 Generator loss: 6.4463
Epoch 0/1... Batch 300/791... Discriminator loss: 0.3302 Generator loss: 6.1946
Batches:  38%|████████████████████████████                                              | 300/791 [06:10<10:09,  1.24s/batch]
Epoch 0/1... Batch 310/791... Discriminator loss: 0.3865 Generator loss: 9.5156
Epoch 0/1... Batch 320/791... Discriminator loss: 0.3643 Generator loss: 15.2904
Epoch 0/1... Batch 330/791... Discriminator loss: 0.3617 Generator loss: 4.7436
Epoch 0/1... Batch 340/791... Discriminator loss: 0.3428 Generator loss: 5.5071
Epoch 0/1... Batch 350/791... Discriminator loss: 0.3278 Generator loss: 10.2527
Epoch 0/1... Batch 360/791... Discriminator loss: 0.3459 Generator loss: 4.5914
Epoch 0/1... Batch 370/791... Discriminator loss: 0.3911 Generator loss: 4.5637
Epoch 0/1... Batch 380/791... Discriminator loss: 0.3335 Generator loss: 5.2410
Epoch 0/1... Batch 390/791... Discriminator loss: 0.4299 Generator loss: 8.5181
Epoch 0/1... Batch 400/791... Discriminator loss: 2.4820 Generator loss: 4.2033
Batches:  51%|█████████████████████████████████████▍                                    | 400/791 [07:20<07:01,  1.08s/batch]
Epoch 0/1... Batch 410/791... Discriminator loss: 1.9930 Generator loss: 0.9494
Epoch 0/1... Batch 420/791... Discriminator loss: 0.9337 Generator loss: 1.5956
Epoch 0/1... Batch 430/791... Discriminator loss: 1.4076 Generator loss: 1.1299
Epoch 0/1... Batch 440/791... Discriminator loss: 1.0554 Generator loss: 1.0217
Epoch 0/1... Batch 450/791... Discriminator loss: 1.2807 Generator loss: 1.0521
Epoch 0/1... Batch 460/791... Discriminator loss: 1.3113 Generator loss: 0.9976
Epoch 0/1... Batch 470/791... Discriminator loss: 1.7538 Generator loss: 1.4148
Epoch 0/1... Batch 480/791... Discriminator loss: 1.3521 Generator loss: 0.9649
Epoch 0/1... Batch 490/791... Discriminator loss: 1.2591 Generator loss: 0.6668
Epoch 0/1... Batch 500/791... Discriminator loss: 1.1729 Generator loss: 1.0440
Batches:  63%|██████████████████████████████████████████████▊                           | 500/791 [08:37<04:47,  1.01batch/s]
Epoch 0/1... Batch 510/791... Discriminator loss: 1.2740 Generator loss: 0.7684
Epoch 0/1... Batch 520/791... Discriminator loss: 1.0771 Generator loss: 1.8000
Epoch 0/1... Batch 530/791... Discriminator loss: 1.1301 Generator loss: 1.1554
Epoch 0/1... Batch 540/791... Discriminator loss: 1.4479 Generator loss: 0.7528
Epoch 0/1... Batch 550/791... Discriminator loss: 1.6097 Generator loss: 1.0192
Epoch 0/1... Batch 560/791... Discriminator loss: 1.1949 Generator loss: 1.1047
Epoch 0/1... Batch 570/791... Discriminator loss: 1.8688 Generator loss: 2.2322
Epoch 0/1... Batch 580/791... Discriminator loss: 1.6142 Generator loss: 0.6179
Epoch 0/1... Batch 590/791... Discriminator loss: 1.4606 Generator loss: 0.8085
Epoch 0/1... Batch 600/791... Discriminator loss: 1.3407 Generator loss: 0.7972
Batches:  76%|████████████████████████████████████████████████████████▏                 | 600/791 [10:37<03:20,  1.05s/batch]
Epoch 0/1... Batch 610/791... Discriminator loss: 1.3856 Generator loss: 0.8172
Epoch 0/1... Batch 620/791... Discriminator loss: 1.5665 Generator loss: 1.4348
Epoch 0/1... Batch 630/791... Discriminator loss: 1.2902 Generator loss: 1.0119
Epoch 0/1... Batch 640/791... Discriminator loss: 1.2913 Generator loss: 1.0471
Epoch 0/1... Batch 650/791... Discriminator loss: 1.3357 Generator loss: 1.7641
Epoch 0/1... Batch 660/791... Discriminator loss: 1.4572 Generator loss: 1.2197
Epoch 0/1... Batch 670/791... Discriminator loss: 1.5018 Generator loss: 1.1212
Epoch 0/1... Batch 680/791... Discriminator loss: 1.2784 Generator loss: 0.8738
Epoch 0/1... Batch 690/791... Discriminator loss: 1.4037 Generator loss: 0.7382
Epoch 0/1... Batch 700/791... Discriminator loss: 1.3033 Generator loss: 0.7524
Batches:  88%|█████████████████████████████████████████████████████████████████▍        | 700/791 [12:34<01:38,  1.08s/batch]
Epoch 0/1... Batch 710/791... Discriminator loss: 1.2799 Generator loss: 0.7301
Epoch 0/1... Batch 720/791... Discriminator loss: 1.4055 Generator loss: 0.7610
Epoch 0/1... Batch 730/791... Discriminator loss: 1.3408 Generator loss: 0.9486
Epoch 0/1... Batch 740/791... Discriminator loss: 1.4732 Generator loss: 0.7711
Epoch 0/1... Batch 750/791... Discriminator loss: 1.2626 Generator loss: 0.9075
Epoch 0/1... Batch 760/791... Discriminator loss: 1.2218 Generator loss: 0.8914
Epoch 0/1... Batch 770/791... Discriminator loss: 1.2315 Generator loss: 0.8463
Epoch 0/1... Batch 780/791... Discriminator loss: 1.5221 Generator loss: 1.1132
Epoch 0/1... Batch 790/791... Discriminator loss: 1.1568 Generator loss: 0.7930
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [14:27<00:00, 867.90s/epoch]
In [17]:
batch_size = 32
z_dim = 200
learning_rate = 0.001
beta1 = 0.5
alpha = 0.2
# the visuals on batch 6300 randomly looks acceptable, but unable to get similar results on second run
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 0.5115 Generator loss: 3.7177
Epoch 0/1... Batch 20/6331... Discriminator loss: 1.4912 Generator loss: 1.4111
Epoch 0/1... Batch 30/6331... Discriminator loss: 1.7850 Generator loss: 0.8752
Epoch 0/1... Batch 40/6331... Discriminator loss: 1.9232 Generator loss: 0.5031
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.2939 Generator loss: 2.2244
Epoch 0/1... Batch 60/6331... Discriminator loss: 1.2366 Generator loss: 1.0862
Epoch 0/1... Batch 70/6331... Discriminator loss: 2.1008 Generator loss: 0.2211
Epoch 0/1... Batch 80/6331... Discriminator loss: 1.8862 Generator loss: 0.7034
Epoch 0/1... Batch 90/6331... Discriminator loss: 2.2137 Generator loss: 0.4439
Epoch 0/1... Batch 100/6331... Discriminator loss: 1.7722 Generator loss: 0.9343
Batches:   2%|█▏                                                                       | 100/6331 [00:14<14:59,  6.92batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 1.5820 Generator loss: 1.2259
Epoch 0/1... Batch 120/6331... Discriminator loss: 1.6295 Generator loss: 0.9286
Epoch 0/1... Batch 130/6331... Discriminator loss: 1.0056 Generator loss: 1.2434
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.9914 Generator loss: 0.3788
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.9521 Generator loss: 0.5895
Epoch 0/1... Batch 160/6331... Discriminator loss: 1.8908 Generator loss: 0.4599
Epoch 0/1... Batch 170/6331... Discriminator loss: 1.7976 Generator loss: 0.5415
Epoch 0/1... Batch 180/6331... Discriminator loss: 1.7391 Generator loss: 0.4484
Epoch 0/1... Batch 190/6331... Discriminator loss: 1.6702 Generator loss: 0.5741
Epoch 0/1... Batch 200/6331... Discriminator loss: 1.8568 Generator loss: 0.4284
Batches:   3%|██▎                                                                      | 200/6331 [00:28<14:35,  7.00batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.5538 Generator loss: 0.5793
Epoch 0/1... Batch 220/6331... Discriminator loss: 1.8365 Generator loss: 0.4889
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.6418 Generator loss: 0.6146
Epoch 0/1... Batch 240/6331... Discriminator loss: 2.0320 Generator loss: 0.3784
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.4182 Generator loss: 0.9006
Epoch 0/1... Batch 260/6331... Discriminator loss: 1.5828 Generator loss: 0.6806
Epoch 0/1... Batch 270/6331... Discriminator loss: 1.6587 Generator loss: 0.5697
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.3113 Generator loss: 0.8651
Epoch 0/1... Batch 290/6331... Discriminator loss: 1.5334 Generator loss: 0.6386
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.4307 Generator loss: 0.7520
Batches:   5%|███▍                                                                     | 300/6331 [00:41<14:00,  7.17batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.8979 Generator loss: 0.5056
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.5771 Generator loss: 0.7117
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.5796 Generator loss: 0.7230
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.6321 Generator loss: 0.6169
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.5606 Generator loss: 0.6677
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.7702 Generator loss: 0.6650
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.5790 Generator loss: 0.7038
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.4638 Generator loss: 0.7664
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.5293 Generator loss: 0.7090
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.5801 Generator loss: 0.6138
Batches:   6%|████▌                                                                    | 400/6331 [00:54<13:30,  7.31batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.7609 Generator loss: 0.6207
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.6922 Generator loss: 0.6316
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.6942 Generator loss: 0.6461
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.6414 Generator loss: 0.6087
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.6476 Generator loss: 0.6879
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.4302 Generator loss: 0.6643
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.5180 Generator loss: 0.7492
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.6794 Generator loss: 0.5900
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.4455 Generator loss: 0.7339
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.4423 Generator loss: 0.7312
Batches:   8%|█████▊                                                                   | 500/6331 [01:07<13:13,  7.35batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.5461 Generator loss: 0.6094
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.5828 Generator loss: 0.6297
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.4993 Generator loss: 0.7926
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.5523 Generator loss: 0.7590
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.5511 Generator loss: 0.7390
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.3548 Generator loss: 0.8218
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.4038 Generator loss: 0.7035
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.5500 Generator loss: 0.7442
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.5012 Generator loss: 0.7046
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.5114 Generator loss: 0.7587
Batches:   9%|██████▉                                                                  | 600/6331 [01:21<12:55,  7.39batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.4858 Generator loss: 0.7497
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.5102 Generator loss: 0.7621
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.4195 Generator loss: 0.8035
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.4353 Generator loss: 0.7575
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.5002 Generator loss: 0.8111
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.4159 Generator loss: 0.8595
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.4254 Generator loss: 0.6462
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.4827 Generator loss: 0.7059
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.4634 Generator loss: 0.7626
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.3053 Generator loss: 0.7935
Batches:  11%|████████                                                                 | 700/6331 [01:35<12:47,  7.33batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.4197 Generator loss: 0.8506
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.4818 Generator loss: 0.6683
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.3824 Generator loss: 0.7635
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.5430 Generator loss: 0.7215
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.4323 Generator loss: 0.7887
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.3281 Generator loss: 0.8785
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.3630 Generator loss: 0.8404
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.5107 Generator loss: 0.7190
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.4673 Generator loss: 0.7107
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.6098 Generator loss: 0.6687
Batches:  13%|█████████▏                                                               | 800/6331 [01:48<12:24,  7.43batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.5027 Generator loss: 0.6197
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.5203 Generator loss: 0.7458
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.5218 Generator loss: 0.6887
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.4076 Generator loss: 0.7757
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.3456 Generator loss: 0.8066
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.4250 Generator loss: 0.7202
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.4575 Generator loss: 0.6826
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.3455 Generator loss: 0.8087
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.4844 Generator loss: 0.6533
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.4557 Generator loss: 0.8936
Batches:  14%|██████████▍                                                              | 900/6331 [02:01<12:05,  7.49batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.4307 Generator loss: 0.7888
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.4431 Generator loss: 0.6561
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.5232 Generator loss: 0.6922
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.5819 Generator loss: 0.6832
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.3929 Generator loss: 0.7799
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.4755 Generator loss: 0.7390
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.4753 Generator loss: 0.6685
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.4142 Generator loss: 0.7800
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.3855 Generator loss: 0.7626
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.5207 Generator loss: 0.7318
Batches:  16%|███████████▎                                                            | 1000/6331 [02:14<11:55,  7.45batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.3316 Generator loss: 0.7956
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.4104 Generator loss: 0.6639
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.5001 Generator loss: 0.6799
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.3970 Generator loss: 0.8744
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.4317 Generator loss: 0.7568
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.5774 Generator loss: 0.7784
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.5774 Generator loss: 0.9344
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.5618 Generator loss: 0.7179
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.5047 Generator loss: 0.7849
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.4877 Generator loss: 0.7700
Batches:  17%|████████████▌                                                           | 1100/6331 [02:28<11:46,  7.40batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.4473 Generator loss: 0.7476
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.5909 Generator loss: 0.6391
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.4345 Generator loss: 0.7766
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.4996 Generator loss: 0.8295
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.3867 Generator loss: 0.8055
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.5432 Generator loss: 0.6405
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.4466 Generator loss: 0.7169
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.4081 Generator loss: 0.8599
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.4908 Generator loss: 0.8855
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.3317 Generator loss: 0.7257
Batches:  19%|█████████████▋                                                          | 1200/6331 [02:42<11:33,  7.40batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.6198 Generator loss: 0.6814
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.4339 Generator loss: 0.7481
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.4611 Generator loss: 0.7509
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.4429 Generator loss: 0.7261
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.4163 Generator loss: 0.7190
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.3996 Generator loss: 0.7694
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.4770 Generator loss: 0.8288
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.5419 Generator loss: 0.7256
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.3928 Generator loss: 0.8494
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.3177 Generator loss: 0.8213
Batches:  21%|██████████████▊                                                         | 1300/6331 [02:55<11:12,  7.48batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.3926 Generator loss: 0.7882
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.3892 Generator loss: 0.7434
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.4325 Generator loss: 0.7457
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.4444 Generator loss: 0.8771
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.4213 Generator loss: 0.7412
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.3957 Generator loss: 0.8960
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.5190 Generator loss: 0.6497
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.4081 Generator loss: 0.7073
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.4349 Generator loss: 0.7674
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.4809 Generator loss: 0.8030
Batches:  22%|███████████████▉                                                        | 1400/6331 [03:08<10:54,  7.54batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.3659 Generator loss: 0.7795
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.4552 Generator loss: 0.8410
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.3530 Generator loss: 0.7698
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.4409 Generator loss: 0.6443
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4438 Generator loss: 0.7865
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.5233 Generator loss: 0.7912
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.5479 Generator loss: 0.5735
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.4904 Generator loss: 0.8310
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4506 Generator loss: 0.6569
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.4125 Generator loss: 0.6423
Batches:  24%|█████████████████                                                       | 1500/6331 [03:21<10:37,  7.58batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.4347 Generator loss: 0.7550
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.4642 Generator loss: 0.6549
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.4747 Generator loss: 0.7166
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.4473 Generator loss: 0.8209
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.4694 Generator loss: 0.7615
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.4222 Generator loss: 0.7286
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.3752 Generator loss: 0.8192
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.5574 Generator loss: 0.6691
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.4623 Generator loss: 0.7325
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.4377 Generator loss: 0.7390
Batches:  25%|██████████████████▏                                                     | 1600/6331 [03:35<10:35,  7.45batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.3858 Generator loss: 0.7584
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.4421 Generator loss: 0.7235
Epoch 0/1... Batch 1630/6331... Discriminator loss: 1.3913 Generator loss: 0.8516
Epoch 0/1... Batch 1640/6331... Discriminator loss: 1.3877 Generator loss: 0.8084
Epoch 0/1... Batch 1650/6331... Discriminator loss: 1.4517 Generator loss: 0.6736
Epoch 0/1... Batch 1660/6331... Discriminator loss: 1.4409 Generator loss: 0.8398
Epoch 0/1... Batch 1670/6331... Discriminator loss: 1.4312 Generator loss: 0.8076
Epoch 0/1... Batch 1680/6331... Discriminator loss: 1.4482 Generator loss: 0.7622
Epoch 0/1... Batch 1690/6331... Discriminator loss: 1.4464 Generator loss: 0.7728
Epoch 0/1... Batch 1700/6331... Discriminator loss: 1.4925 Generator loss: 0.7081
Batches:  27%|███████████████████▎                                                    | 1700/6331 [03:48<10:16,  7.51batch/s]
Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.5591 Generator loss: 0.7775
Epoch 0/1... Batch 1720/6331... Discriminator loss: 1.4785 Generator loss: 0.7310
Epoch 0/1... Batch 1730/6331... Discriminator loss: 1.5514 Generator loss: 0.6312
Epoch 0/1... Batch 1740/6331... Discriminator loss: 1.3874 Generator loss: 0.7589
Epoch 0/1... Batch 1750/6331... Discriminator loss: 1.3827 Generator loss: 0.8208
Epoch 0/1... Batch 1760/6331... Discriminator loss: 1.3898 Generator loss: 0.7681
Epoch 0/1... Batch 1770/6331... Discriminator loss: 1.4094 Generator loss: 0.7054
Epoch 0/1... Batch 1780/6331... Discriminator loss: 1.4216 Generator loss: 0.8237
Epoch 0/1... Batch 1790/6331... Discriminator loss: 1.4110 Generator loss: 0.7237
Epoch 0/1... Batch 1800/6331... Discriminator loss: 1.4009 Generator loss: 0.7863
Batches:  28%|████████████████████▍                                                   | 1800/6331 [04:01<10:01,  7.53batch/s]
Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.4644 Generator loss: 0.8004
Epoch 0/1... Batch 1820/6331... Discriminator loss: 1.4251 Generator loss: 0.7808
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Epoch 0/1... Batch 1900/6331... Discriminator loss: 1.4993 Generator loss: 0.7575
Batches:  30%|█████████████████████▌                                                  | 1900/6331 [04:14<09:46,  7.56batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.4785 Generator loss: 0.7254
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Epoch 0/1... Batch 1980/6331... Discriminator loss: 1.3513 Generator loss: 0.7272
Epoch 0/1... Batch 1990/6331... Discriminator loss: 1.5630 Generator loss: 0.9798
Epoch 0/1... Batch 2000/6331... Discriminator loss: 1.5238 Generator loss: 0.7562
Batches:  32%|██████████████████████▋                                                 | 2000/6331 [04:27<09:34,  7.55batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.4743 Generator loss: 0.6492
Epoch 0/1... Batch 2020/6331... Discriminator loss: 1.5159 Generator loss: 0.7213
Epoch 0/1... Batch 2030/6331... Discriminator loss: 1.4624 Generator loss: 0.7322
Epoch 0/1... Batch 2040/6331... Discriminator loss: 1.5657 Generator loss: 0.6855
Epoch 0/1... Batch 2050/6331... Discriminator loss: 1.5141 Generator loss: 0.6769
Epoch 0/1... Batch 2060/6331... Discriminator loss: 1.4669 Generator loss: 0.7576
Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.4981 Generator loss: 0.8398
Epoch 0/1... Batch 2080/6331... Discriminator loss: 1.5646 Generator loss: 0.8173
Epoch 0/1... Batch 2090/6331... Discriminator loss: 1.4728 Generator loss: 0.6964
Epoch 0/1... Batch 2100/6331... Discriminator loss: 1.5670 Generator loss: 0.6754
Batches:  33%|███████████████████████▉                                                | 2100/6331 [04:41<09:27,  7.46batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.4010 Generator loss: 0.8410
Epoch 0/1... Batch 2120/6331... Discriminator loss: 1.4024 Generator loss: 0.8484
Epoch 0/1... Batch 2130/6331... Discriminator loss: 1.4336 Generator loss: 0.7110
Epoch 0/1... Batch 2140/6331... Discriminator loss: 1.4355 Generator loss: 0.7709
Epoch 0/1... Batch 2150/6331... Discriminator loss: 1.4677 Generator loss: 0.6776
Epoch 0/1... Batch 2160/6331... Discriminator loss: 1.3536 Generator loss: 0.7905
Epoch 0/1... Batch 2170/6331... Discriminator loss: 1.4297 Generator loss: 0.6736
Epoch 0/1... Batch 2180/6331... Discriminator loss: 1.3478 Generator loss: 0.7406
Epoch 0/1... Batch 2190/6331... Discriminator loss: 1.4611 Generator loss: 0.6773
Epoch 0/1... Batch 2200/6331... Discriminator loss: 1.4403 Generator loss: 0.7015
Batches:  35%|█████████████████████████                                               | 2200/6331 [04:55<09:16,  7.42batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.4569 Generator loss: 0.7453
Epoch 0/1... Batch 2220/6331... Discriminator loss: 1.3861 Generator loss: 0.8670
Epoch 0/1... Batch 2230/6331... Discriminator loss: 1.5040 Generator loss: 0.7448
Epoch 0/1... Batch 2240/6331... Discriminator loss: 1.4473 Generator loss: 0.7632
Epoch 0/1... Batch 2250/6331... Discriminator loss: 1.4081 Generator loss: 0.7140
Epoch 0/1... Batch 2260/6331... Discriminator loss: 1.4141 Generator loss: 0.7336
Epoch 0/1... Batch 2270/6331... Discriminator loss: 1.4337 Generator loss: 0.8051
Epoch 0/1... Batch 2280/6331... Discriminator loss: 1.4536 Generator loss: 0.7305
Epoch 0/1... Batch 2290/6331... Discriminator loss: 1.4634 Generator loss: 0.9157
Epoch 0/1... Batch 2300/6331... Discriminator loss: 1.3940 Generator loss: 0.7486
Batches:  36%|██████████████████████████▏                                             | 2300/6331 [05:09<09:06,  7.37batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.4295 Generator loss: 0.8475
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.4791 Generator loss: 0.7901
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.3963 Generator loss: 0.8109
Epoch 0/1... Batch 2340/6331... Discriminator loss: 1.4644 Generator loss: 0.7084
Epoch 0/1... Batch 2350/6331... Discriminator loss: 1.3851 Generator loss: 0.7035
Epoch 0/1... Batch 2360/6331... Discriminator loss: 1.4080 Generator loss: 0.7413
Epoch 0/1... Batch 2370/6331... Discriminator loss: 1.4665 Generator loss: 0.7122
Epoch 0/1... Batch 2380/6331... Discriminator loss: 1.4060 Generator loss: 0.8535
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Epoch 0/1... Batch 2400/6331... Discriminator loss: 1.4332 Generator loss: 0.6739
Batches:  38%|███████████████████████████▎                                            | 2400/6331 [05:22<08:51,  7.40batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.3752 Generator loss: 0.8534
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.5437 Generator loss: 0.6800
Epoch 0/1... Batch 2430/6331... Discriminator loss: 1.5281 Generator loss: 0.7776
Epoch 0/1... Batch 2440/6331... Discriminator loss: 1.4890 Generator loss: 0.6608
Epoch 0/1... Batch 2450/6331... Discriminator loss: 1.4567 Generator loss: 0.7753
Epoch 0/1... Batch 2460/6331... Discriminator loss: 1.4348 Generator loss: 0.7313
Epoch 0/1... Batch 2470/6331... Discriminator loss: 1.4061 Generator loss: 0.7490
Epoch 0/1... Batch 2480/6331... Discriminator loss: 1.3718 Generator loss: 0.9342
Epoch 0/1... Batch 2490/6331... Discriminator loss: 1.4065 Generator loss: 0.8218
Epoch 0/1... Batch 2500/6331... Discriminator loss: 1.4195 Generator loss: 0.8128
Batches:  39%|████████████████████████████▍                                           | 2500/6331 [05:37<08:51,  7.21batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.4318 Generator loss: 0.7665
Epoch 0/1... Batch 2520/6331... Discriminator loss: 1.4734 Generator loss: 0.6687
Epoch 0/1... Batch 2530/6331... Discriminator loss: 1.4447 Generator loss: 0.7500
Epoch 0/1... Batch 2540/6331... Discriminator loss: 1.4625 Generator loss: 0.7400
Epoch 0/1... Batch 2550/6331... Discriminator loss: 1.4143 Generator loss: 0.9831
Epoch 0/1... Batch 2560/6331... Discriminator loss: 1.4985 Generator loss: 0.8184
Epoch 0/1... Batch 2570/6331... Discriminator loss: 1.4212 Generator loss: 0.7469
Epoch 0/1... Batch 2580/6331... Discriminator loss: 1.4870 Generator loss: 0.5722
Epoch 0/1... Batch 2590/6331... Discriminator loss: 1.4239 Generator loss: 0.7539
Epoch 0/1... Batch 2600/6331... Discriminator loss: 1.4190 Generator loss: 0.7560
Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [05:53<08:59,  6.91batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.4025 Generator loss: 0.7857
Epoch 0/1... Batch 2620/6331... Discriminator loss: 1.4376 Generator loss: 0.9321
Epoch 0/1... Batch 2630/6331... Discriminator loss: 1.4462 Generator loss: 0.8611
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Epoch 0/1... Batch 2660/6331... Discriminator loss: 1.4501 Generator loss: 0.7286
Epoch 0/1... Batch 2670/6331... Discriminator loss: 1.4223 Generator loss: 0.8414
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Epoch 0/1... Batch 2700/6331... Discriminator loss: 1.4010 Generator loss: 0.7394
Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [06:07<08:42,  6.95batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.3928 Generator loss: 0.7885
Epoch 0/1... Batch 2720/6331... Discriminator loss: 1.3939 Generator loss: 0.9136
Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.4708 Generator loss: 0.5700
Epoch 0/1... Batch 2740/6331... Discriminator loss: 1.5389 Generator loss: 0.5371
Epoch 0/1... Batch 2750/6331... Discriminator loss: 1.6984 Generator loss: 0.7033
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Epoch 0/1... Batch 2770/6331... Discriminator loss: 1.4112 Generator loss: 0.7972
Epoch 0/1... Batch 2780/6331... Discriminator loss: 1.3972 Generator loss: 0.6983
Epoch 0/1... Batch 2790/6331... Discriminator loss: 1.3746 Generator loss: 0.8133
Epoch 0/1... Batch 2800/6331... Discriminator loss: 1.3752 Generator loss: 0.7292
Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [06:22<08:37,  6.83batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.3752 Generator loss: 0.9040
Epoch 0/1... Batch 2820/6331... Discriminator loss: 1.4096 Generator loss: 0.7668
Epoch 0/1... Batch 2830/6331... Discriminator loss: 1.4645 Generator loss: 0.7317
Epoch 0/1... Batch 2840/6331... Discriminator loss: 1.4139 Generator loss: 0.7036
Epoch 0/1... Batch 2850/6331... Discriminator loss: 1.3924 Generator loss: 0.8873
Epoch 0/1... Batch 2860/6331... Discriminator loss: 1.5617 Generator loss: 0.6605
Epoch 0/1... Batch 2870/6331... Discriminator loss: 1.5177 Generator loss: 0.6903
Epoch 0/1... Batch 2880/6331... Discriminator loss: 1.4118 Generator loss: 0.8508
Epoch 0/1... Batch 2890/6331... Discriminator loss: 1.4751 Generator loss: 0.6774
Epoch 0/1... Batch 2900/6331... Discriminator loss: 1.4537 Generator loss: 0.6782
Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [06:36<08:16,  6.91batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4161 Generator loss: 0.8592
Epoch 0/1... Batch 2920/6331... Discriminator loss: 1.4717 Generator loss: 0.6258
Epoch 0/1... Batch 2930/6331... Discriminator loss: 1.4102 Generator loss: 0.7580
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Epoch 0/1... Batch 2950/6331... Discriminator loss: 1.4147 Generator loss: 0.7844
Epoch 0/1... Batch 2960/6331... Discriminator loss: 1.3757 Generator loss: 0.7912
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Epoch 0/1... Batch 2980/6331... Discriminator loss: 1.4170 Generator loss: 0.7511
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Epoch 0/1... Batch 3000/6331... Discriminator loss: 1.4258 Generator loss: 0.7906
Batches:  47%|██████████████████████████████████                                      | 3000/6331 [06:50<08:00,  6.94batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.4337 Generator loss: 0.6477
Epoch 0/1... Batch 3020/6331... Discriminator loss: 1.3910 Generator loss: 0.8487
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [07:05<07:44,  6.95batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.4201 Generator loss: 0.7893
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [07:19<07:28,  6.98batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4197 Generator loss: 0.7636
Epoch 0/1... Batch 3220/6331... Discriminator loss: 1.4684 Generator loss: 1.0071
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [07:33<07:14,  6.97batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.4265 Generator loss: 0.8407
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [07:48<07:07,  6.86batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.4083 Generator loss: 0.7255
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [08:02<06:46,  6.96batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.3941 Generator loss: 0.7261
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [08:16<06:25,  7.08batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.4061 Generator loss: 0.7498
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Batches:  58%|██████████████████████████████████████████                              | 3700/6331 [08:30<06:11,  7.08batch/s]
Epoch 0/1... Batch 3710/6331... Discriminator loss: 1.4146 Generator loss: 0.7115
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Epoch 0/1... Batch 3810/6331... Discriminator loss: 1.4264 Generator loss: 0.7798
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Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [13:55<00:46,  7.07batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.3702 Generator loss: 0.8223
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3872 Generator loss: 0.7850
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3951 Generator loss: 0.7417
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.3664 Generator loss: 0.7687
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3934 Generator loss: 0.7593
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.4380 Generator loss: 0.8229
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.4070 Generator loss: 0.6733
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.4025 Generator loss: 0.7669
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.3870 Generator loss: 0.8564
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3999 Generator loss: 0.7373
Batches:  96%|█████████████████████████████████████████████████████████████████████▎  | 6100/6331 [14:09<00:32,  7.07batch/s]
Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.4059 Generator loss: 0.7979
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.4033 Generator loss: 0.7269
Epoch 0/1... Batch 6130/6331... Discriminator loss: 1.3948 Generator loss: 0.8672
Epoch 0/1... Batch 6140/6331... Discriminator loss: 1.3976 Generator loss: 0.8344
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Epoch 0/1... Batch 6170/6331... Discriminator loss: 1.4003 Generator loss: 0.7544
Epoch 0/1... Batch 6180/6331... Discriminator loss: 1.3852 Generator loss: 0.7504
Epoch 0/1... Batch 6190/6331... Discriminator loss: 1.3789 Generator loss: 0.8298
Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3890 Generator loss: 0.7069
Batches:  98%|██████████████████████████████████████████████████████████████████████▌ | 6200/6331 [14:24<00:18,  6.93batch/s]
Epoch 0/1... Batch 6210/6331... Discriminator loss: 1.3905 Generator loss: 0.6960
Epoch 0/1... Batch 6220/6331... Discriminator loss: 1.3856 Generator loss: 0.7763
Epoch 0/1... Batch 6230/6331... Discriminator loss: 1.3912 Generator loss: 0.6751
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Epoch 0/1... Batch 6250/6331... Discriminator loss: 1.3757 Generator loss: 0.7971
Epoch 0/1... Batch 6260/6331... Discriminator loss: 1.3939 Generator loss: 0.7445
Epoch 0/1... Batch 6270/6331... Discriminator loss: 1.3869 Generator loss: 0.8083
Epoch 0/1... Batch 6280/6331... Discriminator loss: 1.3830 Generator loss: 0.7983
Epoch 0/1... Batch 6290/6331... Discriminator loss: 1.3817 Generator loss: 0.8186
Epoch 0/1... Batch 6300/6331... Discriminator loss: 1.3894 Generator loss: 0.8088
Batches: 100%|███████████████████████████████████████████████████████████████████████▋| 6300/6331 [14:38<00:04,  7.02batch/s]
Epoch 0/1... Batch 6310/6331... Discriminator loss: 1.4049 Generator loss: 0.7546
Epoch 0/1... Batch 6320/6331... Discriminator loss: 1.3905 Generator loss: 0.8088
Epoch 0/1... Batch 6330/6331... Discriminator loss: 1.3768 Generator loss: 0.7997
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [14:42<00:00, 882.69s/epoch]
In [19]:
batch_size = 32
z_dim = 200
learning_rate = 0.001
beta1 = 0.5
alpha = 0.2
# unable to match the visuals of the previous run for some reason

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 0.5044 Generator loss: 4.1206
Epoch 0/1... Batch 20/6331... Discriminator loss: 2.5716 Generator loss: 1.7344
Epoch 0/1... Batch 30/6331... Discriminator loss: 1.3623 Generator loss: 2.3181
Epoch 0/1... Batch 40/6331... Discriminator loss: 1.1700 Generator loss: 2.5861
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.2700 Generator loss: 2.1419
Epoch 0/1... Batch 60/6331... Discriminator loss: 1.5807 Generator loss: 3.0621
Epoch 0/1... Batch 70/6331... Discriminator loss: 1.5137 Generator loss: 1.0864
Epoch 0/1... Batch 80/6331... Discriminator loss: 1.1109 Generator loss: 0.8963
Epoch 0/1... Batch 90/6331... Discriminator loss: 1.6350 Generator loss: 3.3098
Epoch 0/1... Batch 100/6331... Discriminator loss: 2.0212 Generator loss: 0.5976
Batches:   2%|█▏                                                                       | 100/6331 [00:14<14:46,  7.03batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 1.6211 Generator loss: 2.5513
Epoch 0/1... Batch 120/6331... Discriminator loss: 2.5464 Generator loss: 2.2082
Epoch 0/1... Batch 130/6331... Discriminator loss: 3.7240 Generator loss: 0.0723
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.4602 Generator loss: 0.5043
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.1416 Generator loss: 1.3309
Epoch 0/1... Batch 160/6331... Discriminator loss: 2.3332 Generator loss: 1.2819
Epoch 0/1... Batch 170/6331... Discriminator loss: 1.7067 Generator loss: 0.6473
Epoch 0/1... Batch 180/6331... Discriminator loss: 1.7837 Generator loss: 0.5620
Epoch 0/1... Batch 190/6331... Discriminator loss: 1.6740 Generator loss: 0.6191
Epoch 0/1... Batch 200/6331... Discriminator loss: 1.4184 Generator loss: 0.8758
Batches:   3%|██▎                                                                      | 200/6331 [00:27<14:07,  7.23batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.5115 Generator loss: 0.6955
Epoch 0/1... Batch 220/6331... Discriminator loss: 2.0329 Generator loss: 0.4020
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.3306 Generator loss: 0.8538
Epoch 0/1... Batch 240/6331... Discriminator loss: 1.4250 Generator loss: 0.9303
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.2684 Generator loss: 0.9046
Epoch 0/1... Batch 260/6331... Discriminator loss: 2.0126 Generator loss: 0.4926
Epoch 0/1... Batch 270/6331... Discriminator loss: 1.6497 Generator loss: 0.6497
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.6938 Generator loss: 0.7078
Epoch 0/1... Batch 290/6331... Discriminator loss: 1.6415 Generator loss: 0.6343
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.3235 Generator loss: 0.9469
Batches:   5%|███▍                                                                     | 300/6331 [00:40<13:36,  7.38batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.6571 Generator loss: 0.5934
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.6070 Generator loss: 0.5833
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.5757 Generator loss: 0.6724
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.5104 Generator loss: 0.9249
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.6844 Generator loss: 0.6001
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.4761 Generator loss: 0.6771
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.5973 Generator loss: 0.6548
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.3979 Generator loss: 0.8049
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.5500 Generator loss: 0.7721
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.4877 Generator loss: 0.6667
Batches:   6%|████▌                                                                    | 400/6331 [00:53<13:19,  7.42batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.5855 Generator loss: 0.5881
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.3911 Generator loss: 0.7343
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.5023 Generator loss: 0.5793
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.4787 Generator loss: 0.7153
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.4467 Generator loss: 0.7697
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.4534 Generator loss: 0.6112
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.3819 Generator loss: 0.8482
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.5866 Generator loss: 0.6314
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.7906 Generator loss: 0.5196
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.4754 Generator loss: 0.7509
Batches:   8%|█████▊                                                                   | 500/6331 [01:06<13:03,  7.44batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.4442 Generator loss: 0.7179
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.5375 Generator loss: 0.6713
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.3722 Generator loss: 0.8783
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.5356 Generator loss: 0.7056
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.5583 Generator loss: 0.6964
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.4328 Generator loss: 0.8849
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.3792 Generator loss: 0.8276
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.5893 Generator loss: 0.6000
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.3656 Generator loss: 0.7745
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.4371 Generator loss: 0.7874
Batches:   9%|██████▉                                                                  | 600/6331 [01:19<12:39,  7.54batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.3963 Generator loss: 0.8388
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.4776 Generator loss: 0.6906
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.5750 Generator loss: 0.6893
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.6879 Generator loss: 0.5897
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.6047 Generator loss: 0.6336
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.4282 Generator loss: 0.7359
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.4223 Generator loss: 0.7257
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.7057 Generator loss: 0.5891
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.4539 Generator loss: 0.7572
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.4163 Generator loss: 0.7341
Batches:  11%|████████                                                                 | 700/6331 [01:32<12:26,  7.54batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.6688 Generator loss: 0.5535
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.5756 Generator loss: 0.6039
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.4879 Generator loss: 0.6845
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.4919 Generator loss: 0.7714
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.4611 Generator loss: 0.7287
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.4178 Generator loss: 0.7238
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.4329 Generator loss: 0.9121
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.5194 Generator loss: 0.7347
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.4582 Generator loss: 0.7056
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.4564 Generator loss: 0.8823
Batches:  13%|█████████▏                                                               | 800/6331 [01:45<12:09,  7.58batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.5464 Generator loss: 0.7426
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.5021 Generator loss: 0.6441
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.4522 Generator loss: 0.8113
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.4380 Generator loss: 0.8660
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.4066 Generator loss: 0.8001
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.4052 Generator loss: 0.7386
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.4357 Generator loss: 0.7732
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.4494 Generator loss: 0.7014
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.4250 Generator loss: 0.7745
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.5385 Generator loss: 0.6152
Batches:  14%|██████████▍                                                              | 900/6331 [01:59<12:04,  7.50batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.4565 Generator loss: 0.7123
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.6896 Generator loss: 0.6131
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.5622 Generator loss: 0.8012
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.4494 Generator loss: 0.7379
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.4742 Generator loss: 0.6700
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.5877 Generator loss: 0.6041
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.3682 Generator loss: 0.7343
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.5526 Generator loss: 0.6682
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.5086 Generator loss: 0.7364
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.4250 Generator loss: 0.7659
Batches:  16%|███████████▎                                                            | 1000/6331 [02:13<11:57,  7.43batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.4057 Generator loss: 0.7200
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.5781 Generator loss: 0.6293
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.5758 Generator loss: 0.6869
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.4938 Generator loss: 0.8690
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.4802 Generator loss: 0.6954
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.4697 Generator loss: 0.7706
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.6432 Generator loss: 0.7172
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.4216 Generator loss: 0.7061
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.5250 Generator loss: 0.8408
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.4915 Generator loss: 0.7298
Batches:  17%|████████████▌                                                           | 1100/6331 [02:26<11:42,  7.44batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.4663 Generator loss: 0.7500
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.4500 Generator loss: 0.6810
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.5270 Generator loss: 0.7236
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.5270 Generator loss: 0.7142
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.4693 Generator loss: 0.8434
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.4611 Generator loss: 0.7162
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.4752 Generator loss: 0.6593
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.3863 Generator loss: 0.7861
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.4613 Generator loss: 0.7268
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.4548 Generator loss: 0.6965
Batches:  19%|█████████████▋                                                          | 1200/6331 [02:39<11:22,  7.52batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.6500 Generator loss: 0.6799
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.5171 Generator loss: 0.7151
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.5061 Generator loss: 0.6941
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.3931 Generator loss: 0.7958
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.3935 Generator loss: 0.7035
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.5166 Generator loss: 0.6979
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.4087 Generator loss: 0.8217
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.4132 Generator loss: 0.7570
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.4666 Generator loss: 0.8890
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.4299 Generator loss: 0.8287
Batches:  21%|██████████████▊                                                         | 1300/6331 [02:53<11:13,  7.47batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.4734 Generator loss: 0.9337
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.4099 Generator loss: 0.7270
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.4497 Generator loss: 0.6995
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.4564 Generator loss: 0.9141
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.4805 Generator loss: 0.6930
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.4633 Generator loss: 0.9194
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.4208 Generator loss: 0.8210
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.4505 Generator loss: 0.7011
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.4692 Generator loss: 0.7158
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.4627 Generator loss: 0.7779
Batches:  22%|███████████████▉                                                        | 1400/6331 [03:06<10:53,  7.54batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.3741 Generator loss: 0.7109
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.5362 Generator loss: 0.8485
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.4457 Generator loss: 0.6579
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.3899 Generator loss: 0.7696
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4902 Generator loss: 0.7148
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.4713 Generator loss: 0.6902
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.4205 Generator loss: 0.7676
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.5338 Generator loss: 0.7645
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4346 Generator loss: 0.7409
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.4618 Generator loss: 0.6127
Batches:  24%|█████████████████                                                       | 1500/6331 [03:19<10:36,  7.59batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.3967 Generator loss: 0.7957
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.5395 Generator loss: 0.6828
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.4129 Generator loss: 0.6988
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.3720 Generator loss: 0.8463
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.4807 Generator loss: 0.6425
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.4654 Generator loss: 0.9003
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.5243 Generator loss: 0.8602
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.4837 Generator loss: 0.7593
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.4907 Generator loss: 0.7162
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.4019 Generator loss: 0.7055
Batches:  25%|██████████████████▏                                                     | 1600/6331 [03:32<10:25,  7.56batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.4224 Generator loss: 0.7653
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.4671 Generator loss: 0.6306
Epoch 0/1... Batch 1630/6331... Discriminator loss: 1.4562 Generator loss: 0.7693
Epoch 0/1... Batch 1640/6331... Discriminator loss: 1.4141 Generator loss: 0.7854
Epoch 0/1... Batch 1650/6331... Discriminator loss: 1.3936 Generator loss: 0.8050
Epoch 0/1... Batch 1660/6331... Discriminator loss: 1.4221 Generator loss: 0.7116
Epoch 0/1... Batch 1670/6331... Discriminator loss: 1.4668 Generator loss: 0.8432
Epoch 0/1... Batch 1680/6331... Discriminator loss: 1.4917 Generator loss: 0.7386
Epoch 0/1... Batch 1690/6331... Discriminator loss: 1.4626 Generator loss: 0.7083
Epoch 0/1... Batch 1700/6331... Discriminator loss: 1.5095 Generator loss: 0.7069
Batches:  27%|███████████████████▎                                                    | 1700/6331 [03:45<10:08,  7.61batch/s]
Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.5452 Generator loss: 0.8078
Epoch 0/1... Batch 1720/6331... Discriminator loss: 1.4723 Generator loss: 0.6561
Epoch 0/1... Batch 1730/6331... Discriminator loss: 1.4595 Generator loss: 0.7194
Epoch 0/1... Batch 1740/6331... Discriminator loss: 1.4588 Generator loss: 0.7698
Epoch 0/1... Batch 1750/6331... Discriminator loss: 1.4771 Generator loss: 0.7332
Epoch 0/1... Batch 1760/6331... Discriminator loss: 1.4416 Generator loss: 0.7482
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Epoch 0/1... Batch 1800/6331... Discriminator loss: 1.5276 Generator loss: 0.6933
Batches:  28%|████████████████████▍                                                   | 1800/6331 [03:59<10:05,  7.49batch/s]
Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.3803 Generator loss: 0.7585
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Batches:  30%|█████████████████████▌                                                  | 1900/6331 [04:13<09:57,  7.42batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.3894 Generator loss: 0.7803
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Epoch 0/1... Batch 2000/6331... Discriminator loss: 1.3736 Generator loss: 0.9576
Batches:  32%|██████████████████████▋                                                 | 2000/6331 [04:26<09:38,  7.49batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.6421 Generator loss: 0.9141
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Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.4519 Generator loss: 0.7873
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Batches:  33%|███████████████████████▉                                                | 2100/6331 [04:39<09:20,  7.55batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.4366 Generator loss: 0.7398
Epoch 0/1... Batch 2120/6331... Discriminator loss: 1.4968 Generator loss: 0.7002
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Batches:  35%|█████████████████████████                                               | 2200/6331 [04:52<09:09,  7.52batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.5250 Generator loss: 0.7374
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Batches:  36%|██████████████████████████▏                                             | 2300/6331 [05:06<08:59,  7.48batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.3996 Generator loss: 0.7925
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.4031 Generator loss: 0.8124
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.4525 Generator loss: 0.8020
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Batches:  38%|███████████████████████████▎                                            | 2400/6331 [05:19<08:41,  7.54batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.4533 Generator loss: 0.6925
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.4441 Generator loss: 0.7309
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Batches:  39%|████████████████████████████▍                                           | 2500/6331 [05:32<08:28,  7.54batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.3559 Generator loss: 0.7804
Epoch 0/1... Batch 2520/6331... Discriminator loss: 1.4539 Generator loss: 0.8487
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Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [05:45<08:12,  7.57batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.4260 Generator loss: 0.8232
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Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [05:58<08:01,  7.55batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.4050 Generator loss: 0.8418
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Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.3854 Generator loss: 0.7089
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Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [06:13<08:00,  7.35batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.3977 Generator loss: 1.0860
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Epoch 0/1... Batch 2890/6331... Discriminator loss: 1.3918 Generator loss: 0.8557
Epoch 0/1... Batch 2900/6331... Discriminator loss: 1.4125 Generator loss: 0.6762
Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [06:26<07:40,  7.45batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4087 Generator loss: 0.7639
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Batches:  47%|██████████████████████████████████                                      | 3000/6331 [06:39<07:26,  7.46batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.3989 Generator loss: 0.6626
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [06:52<07:12,  7.48batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.3909 Generator loss: 0.8071
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [07:06<06:58,  7.48batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4052 Generator loss: 0.6506
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [07:19<06:41,  7.55batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.3817 Generator loss: 0.8237
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [07:32<06:28,  7.54batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.4001 Generator loss: 0.8122
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [07:45<06:13,  7.58batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.3955 Generator loss: 0.7720
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [07:58<06:01,  7.56batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.4017 Generator loss: 0.8236
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Epoch 0/1... Batch 3710/6331... Discriminator loss: 1.3764 Generator loss: 0.9372
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Epoch 0/1... Batch 5970/6331... Discriminator loss: 1.3813 Generator loss: 0.8000
Epoch 0/1... Batch 5980/6331... Discriminator loss: 1.3945 Generator loss: 0.7415
Epoch 0/1... Batch 5990/6331... Discriminator loss: 1.4105 Generator loss: 0.8730
Epoch 0/1... Batch 6000/6331... Discriminator loss: 1.3919 Generator loss: 0.8035
Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [13:20<00:44,  7.45batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.3736 Generator loss: 0.8933
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3861 Generator loss: 0.8320
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3947 Generator loss: 0.7531
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.4050 Generator loss: 0.7460
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3982 Generator loss: 0.7382
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.3949 Generator loss: 0.6936
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.3796 Generator loss: 0.8090
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.3804 Generator loss: 0.7835
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.3804 Generator loss: 0.7849
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3951 Generator loss: 0.8271
Batches:  96%|█████████████████████████████████████████████████████████████████████▎  | 6100/6331 [13:33<00:30,  7.50batch/s]
Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.3821 Generator loss: 0.6950
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.4087 Generator loss: 0.7940
Epoch 0/1... Batch 6130/6331... Discriminator loss: 1.3911 Generator loss: 0.8688
Epoch 0/1... Batch 6140/6331... Discriminator loss: 1.3829 Generator loss: 0.8291
Epoch 0/1... Batch 6150/6331... Discriminator loss: 1.4130 Generator loss: 0.6914
Epoch 0/1... Batch 6160/6331... Discriminator loss: 1.3835 Generator loss: 0.7598
Epoch 0/1... Batch 6170/6331... Discriminator loss: 1.3946 Generator loss: 0.7188
Epoch 0/1... Batch 6180/6331... Discriminator loss: 1.4002 Generator loss: 0.8288
Epoch 0/1... Batch 6190/6331... Discriminator loss: 1.3949 Generator loss: 0.7998
Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3742 Generator loss: 0.8707
Batches:  98%|██████████████████████████████████████████████████████████████████████▌ | 6200/6331 [13:47<00:17,  7.49batch/s]
Epoch 0/1... Batch 6210/6331... Discriminator loss: 1.4047 Generator loss: 0.7668
Epoch 0/1... Batch 6220/6331... Discriminator loss: 1.3992 Generator loss: 0.7782
Epoch 0/1... Batch 6230/6331... Discriminator loss: 1.3913 Generator loss: 0.8209
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Epoch 0/1... Batch 6250/6331... Discriminator loss: 1.3893 Generator loss: 0.7982
Epoch 0/1... Batch 6260/6331... Discriminator loss: 1.3881 Generator loss: 0.7692
Epoch 0/1... Batch 6270/6331... Discriminator loss: 1.3867 Generator loss: 0.7545
Epoch 0/1... Batch 6280/6331... Discriminator loss: 1.3848 Generator loss: 0.7290
Epoch 0/1... Batch 6290/6331... Discriminator loss: 1.3814 Generator loss: 0.7539
Epoch 0/1... Batch 6300/6331... Discriminator loss: 1.3867 Generator loss: 0.8238
Batches: 100%|███████████████████████████████████████████████████████████████████████▋| 6300/6331 [14:00<00:04,  7.46batch/s]
Epoch 0/1... Batch 6310/6331... Discriminator loss: 1.3691 Generator loss: 0.7961
Epoch 0/1... Batch 6320/6331... Discriminator loss: 1.3828 Generator loss: 0.7983
Epoch 0/1... Batch 6330/6331... Discriminator loss: 1.3931 Generator loss: 0.7610
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [14:05<00:00, 845.02s/epoch]
In [28]:
batch_size = 32
z_dim = 200
learning_rate = 0.001
beta1 = 0.3 # the losses seem to vary less, but the visuals are still bad
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 10.4056 Generator loss: 0.0001
Epoch 0/1... Batch 20/6331... Discriminator loss: 4.0650 Generator loss: 0.0846
Epoch 0/1... Batch 30/6331... Discriminator loss: 2.5723 Generator loss: 0.1731
Epoch 0/1... Batch 40/6331... Discriminator loss: 1.8835 Generator loss: 0.3129
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.8609 Generator loss: 0.5904
Epoch 0/1... Batch 60/6331... Discriminator loss: 2.0750 Generator loss: 1.4860
Epoch 0/1... Batch 70/6331... Discriminator loss: 2.3003 Generator loss: 0.2562
Epoch 0/1... Batch 80/6331... Discriminator loss: 1.8901 Generator loss: 0.4367
Epoch 0/1... Batch 90/6331... Discriminator loss: 1.8324 Generator loss: 0.6193
Epoch 0/1... Batch 100/6331... Discriminator loss: 1.6307 Generator loss: 0.6006
Batches:   2%|█▏                                                                       | 100/6331 [00:13<14:11,  7.32batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 1.4872 Generator loss: 0.9883
Epoch 0/1... Batch 120/6331... Discriminator loss: 1.6589 Generator loss: 0.6321
Epoch 0/1... Batch 130/6331... Discriminator loss: 1.6368 Generator loss: 0.6155
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.6559 Generator loss: 0.5191
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.6236 Generator loss: 0.6155
Epoch 0/1... Batch 160/6331... Discriminator loss: 1.5269 Generator loss: 0.6294
Epoch 0/1... Batch 170/6331... Discriminator loss: 1.8081 Generator loss: 0.5218
Epoch 0/1... Batch 180/6331... Discriminator loss: 1.8372 Generator loss: 0.4246
Epoch 0/1... Batch 190/6331... Discriminator loss: 1.5381 Generator loss: 0.7126
Epoch 0/1... Batch 200/6331... Discriminator loss: 1.5079 Generator loss: 0.6517
Batches:   3%|██▎                                                                      | 200/6331 [00:27<13:59,  7.31batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.5709 Generator loss: 0.6491
Epoch 0/1... Batch 220/6331... Discriminator loss: 1.5871 Generator loss: 0.7032
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.4959 Generator loss: 0.7392
Epoch 0/1... Batch 240/6331... Discriminator loss: 1.5441 Generator loss: 0.5996
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.6637 Generator loss: 0.5064
Epoch 0/1... Batch 260/6331... Discriminator loss: 1.5341 Generator loss: 0.6727
Epoch 0/1... Batch 270/6331... Discriminator loss: 1.5182 Generator loss: 0.8540
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.4799 Generator loss: 0.7007
Epoch 0/1... Batch 290/6331... Discriminator loss: 1.4984 Generator loss: 0.8754
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.3619 Generator loss: 0.7930
Batches:   5%|███▍                                                                     | 300/6331 [00:41<13:48,  7.28batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.5283 Generator loss: 0.6061
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.4871 Generator loss: 0.6821
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.4720 Generator loss: 0.7444
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.4548 Generator loss: 0.7370
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.4864 Generator loss: 0.6868
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.5173 Generator loss: 0.6263
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.5234 Generator loss: 0.6188
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.5922 Generator loss: 0.5785
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.4578 Generator loss: 0.7792
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.5291 Generator loss: 0.7264
Batches:   6%|████▌                                                                    | 400/6331 [00:57<14:13,  6.95batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.5917 Generator loss: 0.6207
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.5245 Generator loss: 0.7219
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.5410 Generator loss: 0.5125
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.7262 Generator loss: 0.5531
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.4264 Generator loss: 0.7698
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.5525 Generator loss: 0.5575
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.5002 Generator loss: 0.6173
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.5452 Generator loss: 0.8027
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.5993 Generator loss: 0.6056
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.5972 Generator loss: 0.7396
Batches:   8%|█████▊                                                                   | 500/6331 [01:10<13:44,  7.07batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.4630 Generator loss: 0.7994
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.4712 Generator loss: 0.6652
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.4872 Generator loss: 0.5864
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.5692 Generator loss: 0.5892
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.4814 Generator loss: 0.8169
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.5252 Generator loss: 0.5870
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.4432 Generator loss: 0.7207
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.4296 Generator loss: 0.7426
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.4324 Generator loss: 0.7715
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.5296 Generator loss: 0.8390
Batches:   9%|██████▉                                                                  | 600/6331 [01:24<13:20,  7.15batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.4992 Generator loss: 0.7192
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.5412 Generator loss: 0.6327
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.5492 Generator loss: 0.7863
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.5144 Generator loss: 0.8777
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.4731 Generator loss: 0.9081
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.4062 Generator loss: 0.7661
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.4843 Generator loss: 0.7719
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.5631 Generator loss: 0.5578
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.5180 Generator loss: 1.0067
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.5453 Generator loss: 0.7020
Batches:  11%|████████                                                                 | 700/6331 [01:38<13:06,  7.16batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.5132 Generator loss: 0.6286
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.5227 Generator loss: 0.7711
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.5330 Generator loss: 0.5742
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.6402 Generator loss: 0.5182
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.4671 Generator loss: 0.9460
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.4148 Generator loss: 0.7147
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.5048 Generator loss: 0.8152
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.4666 Generator loss: 0.6524
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.5634 Generator loss: 0.5980
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.5071 Generator loss: 0.6656
Batches:  13%|█████████▏                                                               | 800/6331 [01:51<12:45,  7.23batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.4503 Generator loss: 0.7036
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.4744 Generator loss: 0.6592
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.4376 Generator loss: 0.7384
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.4758 Generator loss: 0.7952
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.5332 Generator loss: 0.6907
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.4548 Generator loss: 0.7618
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.4409 Generator loss: 0.5808
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.4599 Generator loss: 0.7021
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.5206 Generator loss: 0.9070
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.5088 Generator loss: 0.7213
Batches:  14%|██████████▍                                                              | 900/6331 [02:06<12:38,  7.16batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.4826 Generator loss: 0.8321
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.4348 Generator loss: 0.7238
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.5093 Generator loss: 0.5972
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.4724 Generator loss: 0.8674
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.6190 Generator loss: 0.6238
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.5462 Generator loss: 0.6250
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.4397 Generator loss: 0.6141
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.4547 Generator loss: 0.7054
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.5129 Generator loss: 0.5681
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.7008 Generator loss: 0.5409
Batches:  16%|███████████▎                                                            | 1000/6331 [02:20<12:30,  7.11batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.5509 Generator loss: 0.5441
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.4225 Generator loss: 0.8024
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.4724 Generator loss: 0.7372
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.4208 Generator loss: 0.7847
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.4231 Generator loss: 0.8531
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.4328 Generator loss: 0.9685
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.4399 Generator loss: 0.8271
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.4731 Generator loss: 0.6983
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.3947 Generator loss: 0.6943
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.4738 Generator loss: 0.5916
Batches:  17%|████████████▌                                                           | 1100/6331 [02:34<12:16,  7.10batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.6057 Generator loss: 1.2012
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.5004 Generator loss: 0.9172
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.4520 Generator loss: 0.8493
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.5927 Generator loss: 0.9152
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.4148 Generator loss: 0.7432
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.3848 Generator loss: 0.7263
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.5048 Generator loss: 0.7542
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.4680 Generator loss: 0.8510
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.5222 Generator loss: 0.6151
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.4514 Generator loss: 0.6726
Batches:  19%|█████████████▋                                                          | 1200/6331 [02:48<11:57,  7.15batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.6031 Generator loss: 0.6141
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.3806 Generator loss: 0.8728
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.4598 Generator loss: 0.8108
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.4359 Generator loss: 0.7651
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.4061 Generator loss: 0.7846
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.4528 Generator loss: 0.6714
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.4308 Generator loss: 0.8223
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.4720 Generator loss: 0.8711
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.4423 Generator loss: 0.7576
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.4429 Generator loss: 0.7720
Batches:  21%|██████████████▊                                                         | 1300/6331 [03:02<11:52,  7.06batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.3938 Generator loss: 0.7813
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.3964 Generator loss: 0.7614
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.4033 Generator loss: 0.8593
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.4577 Generator loss: 0.7541
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.4566 Generator loss: 0.6341
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.4696 Generator loss: 0.9610
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.4018 Generator loss: 0.8148
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.3748 Generator loss: 0.8001
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.5421 Generator loss: 0.6139
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.4715 Generator loss: 0.9179
Batches:  22%|███████████████▉                                                        | 1400/6331 [03:16<11:30,  7.14batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.4336 Generator loss: 0.7580
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.4289 Generator loss: 0.7811
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.4412 Generator loss: 0.6539
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.4338 Generator loss: 1.0129
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4449 Generator loss: 0.7352
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.4104 Generator loss: 0.8365
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.4471 Generator loss: 0.6600
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.3774 Generator loss: 0.9315
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4521 Generator loss: 0.5746
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.5247 Generator loss: 0.8298
Batches:  24%|█████████████████                                                       | 1500/6331 [03:30<11:15,  7.15batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.4844 Generator loss: 0.8657
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.5331 Generator loss: 0.5291
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.5163 Generator loss: 0.7155
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.4453 Generator loss: 0.6720
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.4509 Generator loss: 0.6940
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.4101 Generator loss: 0.7391
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.3894 Generator loss: 0.7888
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.4223 Generator loss: 0.6921
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.4233 Generator loss: 0.9760
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.3769 Generator loss: 0.7703
Batches:  25%|██████████████████▏                                                     | 1600/6331 [03:43<10:52,  7.25batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.5204 Generator loss: 0.4922
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.4654 Generator loss: 0.6509
Epoch 0/1... Batch 1630/6331... Discriminator loss: 1.6136 Generator loss: 0.7728
Epoch 0/1... Batch 1640/6331... Discriminator loss: 1.4345 Generator loss: 0.7324
Epoch 0/1... Batch 1650/6331... Discriminator loss: 1.4994 Generator loss: 0.7754
Epoch 0/1... Batch 1660/6331... Discriminator loss: 1.4876 Generator loss: 0.8990
Epoch 0/1... Batch 1670/6331... Discriminator loss: 1.4251 Generator loss: 0.9123
Epoch 0/1... Batch 1680/6331... Discriminator loss: 1.4389 Generator loss: 0.6545
Epoch 0/1... Batch 1690/6331... Discriminator loss: 1.4413 Generator loss: 0.7340
Epoch 0/1... Batch 1700/6331... Discriminator loss: 1.4269 Generator loss: 0.6930
Batches:  27%|███████████████████▎                                                    | 1700/6331 [03:56<10:29,  7.36batch/s]
Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.4803 Generator loss: 0.6196
Epoch 0/1... Batch 1720/6331... Discriminator loss: 1.4337 Generator loss: 0.7596
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Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.3717 Generator loss: 0.7453
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Batches:  30%|█████████████████████▌                                                  | 1900/6331 [04:23<09:57,  7.41batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.5473 Generator loss: 0.8159
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Batches:  32%|██████████████████████▋                                                 | 2000/6331 [04:37<09:49,  7.35batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.4311 Generator loss: 0.6676
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Epoch 0/1... Batch 2040/6331... Discriminator loss: 1.4414 Generator loss: 0.7429
Epoch 0/1... Batch 2050/6331... Discriminator loss: 1.4060 Generator loss: 0.7998
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Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.5168 Generator loss: 0.6084
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Batches:  33%|███████████████████████▉                                                | 2100/6331 [04:51<09:40,  7.29batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.4367 Generator loss: 0.6885
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Epoch 0/1... Batch 2170/6331... Discriminator loss: 1.4357 Generator loss: 0.8401
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Batches:  35%|█████████████████████████                                               | 2200/6331 [05:06<09:40,  7.12batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.4386 Generator loss: 0.8512
Epoch 0/1... Batch 2220/6331... Discriminator loss: 1.4268 Generator loss: 0.7682
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Epoch 0/1... Batch 2260/6331... Discriminator loss: 1.4607 Generator loss: 0.6504
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Epoch 0/1... Batch 2300/6331... Discriminator loss: 1.4453 Generator loss: 0.8673
Batches:  36%|██████████████████████████▏                                             | 2300/6331 [05:20<09:25,  7.13batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.3991 Generator loss: 0.7876
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.4252 Generator loss: 0.6526
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.4429 Generator loss: 1.0704
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Batches:  38%|███████████████████████████▎                                            | 2400/6331 [05:34<09:18,  7.04batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.3865 Generator loss: 0.8841
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.4098 Generator loss: 0.7615
Epoch 0/1... Batch 2430/6331... Discriminator loss: 1.4497 Generator loss: 0.8348
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Epoch 0/1... Batch 2470/6331... Discriminator loss: 1.3929 Generator loss: 0.7154
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Epoch 0/1... Batch 2490/6331... Discriminator loss: 1.3773 Generator loss: 0.7395
Epoch 0/1... Batch 2500/6331... Discriminator loss: 1.4279 Generator loss: 0.9215
Batches:  39%|████████████████████████████▍                                           | 2500/6331 [05:48<08:57,  7.13batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.4344 Generator loss: 0.7658
Epoch 0/1... Batch 2520/6331... Discriminator loss: 1.4317 Generator loss: 0.7120
Epoch 0/1... Batch 2530/6331... Discriminator loss: 1.3978 Generator loss: 0.7340
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Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [06:02<08:44,  7.11batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.4128 Generator loss: 0.7531
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Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [06:16<08:25,  7.19batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.3937 Generator loss: 0.7934
Epoch 0/1... Batch 2720/6331... Discriminator loss: 1.4091 Generator loss: 0.8337
Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.4026 Generator loss: 0.7650
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Epoch 0/1... Batch 2800/6331... Discriminator loss: 1.4619 Generator loss: 0.6769
Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [06:30<08:11,  7.18batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.4331 Generator loss: 0.6889
Epoch 0/1... Batch 2820/6331... Discriminator loss: 1.4311 Generator loss: 0.8029
Epoch 0/1... Batch 2830/6331... Discriminator loss: 1.4052 Generator loss: 0.7512
Epoch 0/1... Batch 2840/6331... Discriminator loss: 1.4226 Generator loss: 0.6663
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Epoch 0/1... Batch 2860/6331... Discriminator loss: 1.3886 Generator loss: 0.7629
Epoch 0/1... Batch 2870/6331... Discriminator loss: 1.4981 Generator loss: 0.9296
Epoch 0/1... Batch 2880/6331... Discriminator loss: 1.4204 Generator loss: 0.8574
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Epoch 0/1... Batch 2900/6331... Discriminator loss: 1.4093 Generator loss: 0.6824
Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [06:43<07:51,  7.28batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4011 Generator loss: 0.7836
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Batches:  47%|██████████████████████████████████                                      | 3000/6331 [06:58<07:47,  7.12batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.4012 Generator loss: 0.6853
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [07:12<07:38,  7.05batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.4183 Generator loss: 0.6973
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [07:27<07:30,  6.95batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4226 Generator loss: 0.7501
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [07:41<07:12,  7.01batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.4072 Generator loss: 0.8941
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [07:55<06:57,  7.01batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.4240 Generator loss: 0.7278
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [08:10<06:44,  7.00batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.3948 Generator loss: 0.7316
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [08:24<06:29,  7.02batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.3916 Generator loss: 0.8231
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Batches:  58%|██████████████████████████████████████████                              | 3700/6331 [08:38<06:12,  7.06batch/s]
Epoch 0/1... Batch 3710/6331... Discriminator loss: 1.3830 Generator loss: 0.7770
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Epoch 0/1... Batch 4910/6331... Discriminator loss: 1.4134 Generator loss: 0.7451
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Epoch 0/1... Batch 5930/6331... Discriminator loss: 1.3938 Generator loss: 0.7889
Epoch 0/1... Batch 5940/6331... Discriminator loss: 1.3849 Generator loss: 0.7344
Epoch 0/1... Batch 5950/6331... Discriminator loss: 1.4028 Generator loss: 0.7119
Epoch 0/1... Batch 5960/6331... Discriminator loss: 1.4006 Generator loss: 0.7581
Epoch 0/1... Batch 5970/6331... Discriminator loss: 1.3942 Generator loss: 0.7800
Epoch 0/1... Batch 5980/6331... Discriminator loss: 1.3805 Generator loss: 0.7978
Epoch 0/1... Batch 5990/6331... Discriminator loss: 1.3888 Generator loss: 0.8781
Epoch 0/1... Batch 6000/6331... Discriminator loss: 1.3829 Generator loss: 0.7691
Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [14:04<00:47,  7.03batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.3963 Generator loss: 0.8535
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3805 Generator loss: 0.7818
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3996 Generator loss: 0.6844
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.3947 Generator loss: 0.8159
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3862 Generator loss: 0.8018
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.4071 Generator loss: 0.7449
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.3894 Generator loss: 0.7280
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.3982 Generator loss: 0.7345
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.4048 Generator loss: 0.8767
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3846 Generator loss: 0.7434
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Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.3758 Generator loss: 0.7918
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.3841 Generator loss: 0.7486
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Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3931 Generator loss: 0.7898
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Epoch 0/1... Batch 6300/6331... Discriminator loss: 1.3926 Generator loss: 0.8907
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Epoch 0/1... Batch 6330/6331... Discriminator loss: 1.3940 Generator loss: 0.8244
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [14:50<00:00, 890.46s/epoch]
In [18]:
batch_size = 32
z_dim = 200
learning_rate = 0.002
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 5.4785 Generator loss: 0.0145
Epoch 0/1... Batch 20/6331... Discriminator loss: 3.1037 Generator loss: 0.2022
Epoch 0/1... Batch 30/6331... Discriminator loss: 1.5078 Generator loss: 3.5222
Epoch 0/1... Batch 40/6331... Discriminator loss: 1.8756 Generator loss: 0.3056
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.4507 Generator loss: 0.6708
Epoch 0/1... Batch 60/6331... Discriminator loss: 2.0680 Generator loss: 3.3547
Epoch 0/1... Batch 70/6331... Discriminator loss: 1.1757 Generator loss: 0.8846
Epoch 0/1... Batch 80/6331... Discriminator loss: 1.7818 Generator loss: 0.3975
Epoch 0/1... Batch 90/6331... Discriminator loss: 1.9203 Generator loss: 0.4779
Epoch 0/1... Batch 100/6331... Discriminator loss: 1.3721 Generator loss: 1.3006
Batches:   2%|█▏                                                                       | 100/6331 [00:14<15:02,  6.90batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 2.1100 Generator loss: 2.6391
Epoch 0/1... Batch 120/6331... Discriminator loss: 1.0850 Generator loss: 1.1347
Epoch 0/1... Batch 130/6331... Discriminator loss: 1.2320 Generator loss: 1.1427
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.9154 Generator loss: 0.4311
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.6456 Generator loss: 0.6091
Epoch 0/1... Batch 160/6331... Discriminator loss: 1.3764 Generator loss: 0.5449
Epoch 0/1... Batch 170/6331... Discriminator loss: 1.9273 Generator loss: 1.0420
Epoch 0/1... Batch 180/6331... Discriminator loss: 2.5397 Generator loss: 0.1762
Epoch 0/1... Batch 190/6331... Discriminator loss: 1.8594 Generator loss: 0.7574
Epoch 0/1... Batch 200/6331... Discriminator loss: 1.3051 Generator loss: 0.6759
Batches:   3%|██▎                                                                      | 200/6331 [00:28<14:34,  7.01batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.8203 Generator loss: 0.5784
Epoch 0/1... Batch 220/6331... Discriminator loss: 2.0253 Generator loss: 1.1097
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.8859 Generator loss: 0.6231
Epoch 0/1... Batch 240/6331... Discriminator loss: 1.9620 Generator loss: 0.4044
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.4035 Generator loss: 0.9282
Epoch 0/1... Batch 260/6331... Discriminator loss: 1.1818 Generator loss: 0.9234
Epoch 0/1... Batch 270/6331... Discriminator loss: 2.4885 Generator loss: 0.4937
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.7995 Generator loss: 1.0746
Epoch 0/1... Batch 290/6331... Discriminator loss: 2.1149 Generator loss: 1.3782
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.3965 Generator loss: 0.7276
Batches:   5%|███▍                                                                     | 300/6331 [00:42<14:15,  7.05batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.6824 Generator loss: 0.4958
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.6539 Generator loss: 0.9383
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.6468 Generator loss: 0.5177
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.5757 Generator loss: 0.4954
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.6905 Generator loss: 0.5806
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.7350 Generator loss: 0.4691
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.2261 Generator loss: 0.8629
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.4403 Generator loss: 0.6781
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.4816 Generator loss: 0.6680
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.5251 Generator loss: 0.7345
Batches:   6%|████▌                                                                    | 400/6331 [00:55<13:47,  7.17batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.3398 Generator loss: 0.9800
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.2415 Generator loss: 0.7549
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.4504 Generator loss: 0.8302
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.3993 Generator loss: 0.7752
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.5410 Generator loss: 0.7830
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.5340 Generator loss: 0.6813
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.6678 Generator loss: 0.4724
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.5147 Generator loss: 0.5970
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.7731 Generator loss: 0.9161
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.6655 Generator loss: 0.5698
Batches:   8%|█████▊                                                                   | 500/6331 [01:09<13:27,  7.22batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.4102 Generator loss: 0.7118
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.5782 Generator loss: 0.8086
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.5106 Generator loss: 0.7364
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.3035 Generator loss: 0.9016
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.4435 Generator loss: 0.9248
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.4184 Generator loss: 0.6294
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.3414 Generator loss: 0.7565
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.4822 Generator loss: 0.7554
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.4856 Generator loss: 0.6337
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.5225 Generator loss: 0.7515
Batches:   9%|██████▉                                                                  | 600/6331 [01:23<13:22,  7.15batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.3645 Generator loss: 0.7663
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.4181 Generator loss: 0.7817
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.4087 Generator loss: 0.8593
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.5228 Generator loss: 0.7267
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.4235 Generator loss: 0.7614
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.4611 Generator loss: 0.9028
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.4396 Generator loss: 0.7613
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.4631 Generator loss: 0.8910
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.5109 Generator loss: 0.9115
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.5835 Generator loss: 0.6110
Batches:  11%|████████                                                                 | 700/6331 [01:37<13:04,  7.17batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.5556 Generator loss: 0.6940
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.5851 Generator loss: 0.6734
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.4037 Generator loss: 0.6884
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.5912 Generator loss: 0.5759
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.3493 Generator loss: 1.1394
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.3544 Generator loss: 0.7988
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.4470 Generator loss: 0.7187
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.3541 Generator loss: 0.7360
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.4936 Generator loss: 0.8309
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.4669 Generator loss: 0.8257
Batches:  13%|█████████▏                                                               | 800/6331 [01:51<12:54,  7.14batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.5377 Generator loss: 0.6249
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.5879 Generator loss: 0.6793
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.4583 Generator loss: 0.8734
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.3445 Generator loss: 0.8125
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.4645 Generator loss: 0.7608
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.3824 Generator loss: 0.9079
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.4926 Generator loss: 0.6164
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.5338 Generator loss: 0.9245
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.5977 Generator loss: 0.9574
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.5118 Generator loss: 0.5517
Batches:  14%|██████████▍                                                              | 900/6331 [02:05<12:34,  7.20batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.4349 Generator loss: 0.8191
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.3638 Generator loss: 0.8880
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.4962 Generator loss: 0.7921
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.3572 Generator loss: 0.7586
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.6041 Generator loss: 0.5142
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.3930 Generator loss: 0.8362
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.3249 Generator loss: 0.8692
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.5332 Generator loss: 0.5877
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.3610 Generator loss: 0.7264
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.4786 Generator loss: 0.7161
Batches:  16%|███████████▎                                                            | 1000/6331 [02:18<12:14,  7.26batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.3712 Generator loss: 0.7698
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.4786 Generator loss: 0.7699
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.5091 Generator loss: 0.7302
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.4996 Generator loss: 0.7371
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.3137 Generator loss: 1.0486
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.4217 Generator loss: 0.6841
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.4736 Generator loss: 0.9324
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.4199 Generator loss: 0.6514
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.4991 Generator loss: 0.7126
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.5879 Generator loss: 0.8344
Batches:  17%|████████████▌                                                           | 1100/6331 [02:32<11:56,  7.30batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.7027 Generator loss: 0.9369
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.4510 Generator loss: 0.6152
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.5151 Generator loss: 0.6996
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.5073 Generator loss: 0.7020
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.3323 Generator loss: 0.7635
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.5405 Generator loss: 0.7419
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.5885 Generator loss: 0.8452
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.4360 Generator loss: 0.7910
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.6760 Generator loss: 0.3732
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.4993 Generator loss: 0.6289
Batches:  19%|█████████████▋                                                          | 1200/6331 [02:45<11:42,  7.30batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.7680 Generator loss: 0.3915
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.3818 Generator loss: 0.7519
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.5352 Generator loss: 0.6993
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.4570 Generator loss: 0.6567
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.5662 Generator loss: 0.6292
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.4840 Generator loss: 0.8351
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.4353 Generator loss: 0.7892
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.5086 Generator loss: 0.8928
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.4343 Generator loss: 0.7515
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.4217 Generator loss: 0.7400
Batches:  21%|██████████████▊                                                         | 1300/6331 [03:00<11:38,  7.21batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.5274 Generator loss: 0.5842
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.5962 Generator loss: 0.6955
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.5567 Generator loss: 0.5699
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.4666 Generator loss: 0.7142
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.4870 Generator loss: 0.7878
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.3343 Generator loss: 0.7074
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.3833 Generator loss: 0.6479
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.4168 Generator loss: 0.8601
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.5446 Generator loss: 0.6278
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.7162 Generator loss: 0.5753
Batches:  22%|███████████████▉                                                        | 1400/6331 [03:14<11:26,  7.19batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.5460 Generator loss: 0.6275
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.4580 Generator loss: 0.7785
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.3147 Generator loss: 0.8684
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.3520 Generator loss: 0.7639
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4829 Generator loss: 0.6683
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.3833 Generator loss: 0.9519
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.4133 Generator loss: 0.8412
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.4001 Generator loss: 0.8836
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4301 Generator loss: 0.6565
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.4533 Generator loss: 0.6802
Batches:  24%|█████████████████                                                       | 1500/6331 [03:28<11:17,  7.13batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.4536 Generator loss: 0.6327
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.4404 Generator loss: 0.6506
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.4471 Generator loss: 0.6600
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.3865 Generator loss: 0.8323
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.4095 Generator loss: 0.7857
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.5187 Generator loss: 0.7263
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.3545 Generator loss: 0.8066
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.4724 Generator loss: 0.7154
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.3603 Generator loss: 0.8660
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.5263 Generator loss: 0.5791
Batches:  25%|██████████████████▏                                                     | 1600/6331 [03:42<11:07,  7.09batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.5416 Generator loss: 0.6911
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.3653 Generator loss: 0.7032
Epoch 0/1... Batch 1630/6331... Discriminator loss: 1.3337 Generator loss: 0.7576
Epoch 0/1... Batch 1640/6331... Discriminator loss: 1.4095 Generator loss: 0.8439
Epoch 0/1... Batch 1650/6331... Discriminator loss: 1.3936 Generator loss: 0.7833
Epoch 0/1... Batch 1660/6331... Discriminator loss: 1.3462 Generator loss: 0.8430
Epoch 0/1... Batch 1670/6331... Discriminator loss: 1.4780 Generator loss: 0.6042
Epoch 0/1... Batch 1680/6331... Discriminator loss: 1.4034 Generator loss: 0.7872
Epoch 0/1... Batch 1690/6331... Discriminator loss: 1.4923 Generator loss: 0.7566
Epoch 0/1... Batch 1700/6331... Discriminator loss: 1.4129 Generator loss: 0.6990
Batches:  27%|███████████████████▎                                                    | 1700/6331 [03:56<10:52,  7.10batch/s]
Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.4521 Generator loss: 0.8650
Epoch 0/1... Batch 1720/6331... Discriminator loss: 1.4079 Generator loss: 0.7417
Epoch 0/1... Batch 1730/6331... Discriminator loss: 1.3902 Generator loss: 0.7402
Epoch 0/1... Batch 1740/6331... Discriminator loss: 1.4171 Generator loss: 0.8067
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Epoch 0/1... Batch 1770/6331... Discriminator loss: 1.4195 Generator loss: 0.9733
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Epoch 0/1... Batch 1800/6331... Discriminator loss: 1.4890 Generator loss: 0.8039
Batches:  28%|████████████████████▍                                                   | 1800/6331 [04:10<10:35,  7.12batch/s]
Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.4081 Generator loss: 0.9435
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Batches:  30%|█████████████████████▌                                                  | 1900/6331 [04:25<10:32,  7.01batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.3780 Generator loss: 0.8287
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Epoch 0/1... Batch 2000/6331... Discriminator loss: 1.4154 Generator loss: 0.7561
Batches:  32%|██████████████████████▋                                                 | 2000/6331 [04:39<10:17,  7.02batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.3957 Generator loss: 0.7453
Epoch 0/1... Batch 2020/6331... Discriminator loss: 1.5164 Generator loss: 0.6719
Epoch 0/1... Batch 2030/6331... Discriminator loss: 1.4271 Generator loss: 0.7100
Epoch 0/1... Batch 2040/6331... Discriminator loss: 1.4245 Generator loss: 0.7577
Epoch 0/1... Batch 2050/6331... Discriminator loss: 1.4767 Generator loss: 0.8206
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Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.4301 Generator loss: 0.7930
Epoch 0/1... Batch 2080/6331... Discriminator loss: 1.5929 Generator loss: 0.6690
Epoch 0/1... Batch 2090/6331... Discriminator loss: 1.5046 Generator loss: 0.6242
Epoch 0/1... Batch 2100/6331... Discriminator loss: 1.4342 Generator loss: 0.8479
Batches:  33%|███████████████████████▉                                                | 2100/6331 [04:53<09:55,  7.11batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.4110 Generator loss: 0.7373
Epoch 0/1... Batch 2120/6331... Discriminator loss: 1.3937 Generator loss: 0.8001
Epoch 0/1... Batch 2130/6331... Discriminator loss: 1.3300 Generator loss: 0.7522
Epoch 0/1... Batch 2140/6331... Discriminator loss: 1.4022 Generator loss: 0.8527
Epoch 0/1... Batch 2150/6331... Discriminator loss: 1.4720 Generator loss: 0.6698
Epoch 0/1... Batch 2160/6331... Discriminator loss: 1.4285 Generator loss: 0.7193
Epoch 0/1... Batch 2170/6331... Discriminator loss: 1.4205 Generator loss: 0.8533
Epoch 0/1... Batch 2180/6331... Discriminator loss: 1.3295 Generator loss: 0.8205
Epoch 0/1... Batch 2190/6331... Discriminator loss: 1.3768 Generator loss: 0.7728
Epoch 0/1... Batch 2200/6331... Discriminator loss: 1.4755 Generator loss: 0.8110
Batches:  35%|█████████████████████████                                               | 2200/6331 [05:06<09:30,  7.24batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.4216 Generator loss: 0.7966
Epoch 0/1... Batch 2220/6331... Discriminator loss: 1.3953 Generator loss: 0.7284
Epoch 0/1... Batch 2230/6331... Discriminator loss: 1.5052 Generator loss: 0.7073
Epoch 0/1... Batch 2240/6331... Discriminator loss: 1.5375 Generator loss: 0.6304
Epoch 0/1... Batch 2250/6331... Discriminator loss: 1.5472 Generator loss: 0.7199
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Epoch 0/1... Batch 2270/6331... Discriminator loss: 1.3919 Generator loss: 0.8223
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Epoch 0/1... Batch 2300/6331... Discriminator loss: 1.4106 Generator loss: 0.7906
Batches:  36%|██████████████████████████▏                                             | 2300/6331 [05:20<09:13,  7.29batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.4172 Generator loss: 0.7939
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.4375 Generator loss: 0.7452
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.4618 Generator loss: 0.7489
Epoch 0/1... Batch 2340/6331... Discriminator loss: 1.3861 Generator loss: 0.7637
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Epoch 0/1... Batch 2360/6331... Discriminator loss: 1.4453 Generator loss: 0.7297
Epoch 0/1... Batch 2370/6331... Discriminator loss: 1.3903 Generator loss: 0.8429
Epoch 0/1... Batch 2380/6331... Discriminator loss: 1.3994 Generator loss: 0.8135
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Epoch 0/1... Batch 2400/6331... Discriminator loss: 1.4195 Generator loss: 0.7667
Batches:  38%|███████████████████████████▎                                            | 2400/6331 [05:34<09:04,  7.22batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.4053 Generator loss: 0.6851
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.4230 Generator loss: 0.7761
Epoch 0/1... Batch 2430/6331... Discriminator loss: 1.4132 Generator loss: 0.7233
Epoch 0/1... Batch 2440/6331... Discriminator loss: 1.4424 Generator loss: 0.8060
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Epoch 0/1... Batch 2480/6331... Discriminator loss: 1.3896 Generator loss: 0.7554
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Epoch 0/1... Batch 2500/6331... Discriminator loss: 1.4347 Generator loss: 0.7161
Batches:  39%|████████████████████████████▍                                           | 2500/6331 [05:48<08:58,  7.12batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.3734 Generator loss: 0.8456
Epoch 0/1... Batch 2520/6331... Discriminator loss: 1.3674 Generator loss: 0.8410
Epoch 0/1... Batch 2530/6331... Discriminator loss: 1.4345 Generator loss: 0.7087
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Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [06:03<08:50,  7.04batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.3985 Generator loss: 0.7784
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Epoch 0/1... Batch 2660/6331... Discriminator loss: 1.3651 Generator loss: 0.7896
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Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [06:16<08:29,  7.12batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.3937 Generator loss: 0.6670
Epoch 0/1... Batch 2720/6331... Discriminator loss: 1.4176 Generator loss: 0.8211
Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.3886 Generator loss: 0.8005
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Epoch 0/1... Batch 2770/6331... Discriminator loss: 1.3896 Generator loss: 0.7391
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Epoch 0/1... Batch 2800/6331... Discriminator loss: 1.4336 Generator loss: 0.8063
Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [06:30<08:09,  7.21batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.4316 Generator loss: 0.7838
Epoch 0/1... Batch 2820/6331... Discriminator loss: 1.4228 Generator loss: 0.7408
Epoch 0/1... Batch 2830/6331... Discriminator loss: 1.3844 Generator loss: 0.7070
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Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [06:44<07:55,  7.21batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4065 Generator loss: 0.8018
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Batches:  47%|██████████████████████████████████                                      | 3000/6331 [06:57<07:38,  7.26batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.4237 Generator loss: 0.7746
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [07:10<07:19,  7.36batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.3963 Generator loss: 0.6594
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [07:24<07:02,  7.42batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4268 Generator loss: 0.7316
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [07:37<06:46,  7.46batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.3776 Generator loss: 0.8324
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [07:51<06:39,  7.34batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.4995 Generator loss: 0.7412
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [08:05<06:25,  7.34batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.3558 Generator loss: 0.7833
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [08:18<06:08,  7.40batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.4274 Generator loss: 0.7736
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Epoch 0/1... Batch 3710/6331... Discriminator loss: 1.3925 Generator loss: 0.7814
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Batches:  93%|███████████████████████████████████████████████████████████████████     | 5900/6331 [13:33<00:59,  7.28batch/s]
Epoch 0/1... Batch 5910/6331... Discriminator loss: 1.3800 Generator loss: 0.7749
Epoch 0/1... Batch 5920/6331... Discriminator loss: 1.3634 Generator loss: 0.8167
Epoch 0/1... Batch 5930/6331... Discriminator loss: 1.3916 Generator loss: 0.7811
Epoch 0/1... Batch 5940/6331... Discriminator loss: 1.3769 Generator loss: 0.8052
Epoch 0/1... Batch 5950/6331... Discriminator loss: 1.3968 Generator loss: 0.7690
Epoch 0/1... Batch 5960/6331... Discriminator loss: 1.4065 Generator loss: 0.7867
Epoch 0/1... Batch 5970/6331... Discriminator loss: 1.3858 Generator loss: 0.7708
Epoch 0/1... Batch 5980/6331... Discriminator loss: 1.3810 Generator loss: 0.7953
Epoch 0/1... Batch 5990/6331... Discriminator loss: 1.3824 Generator loss: 0.7623
Epoch 0/1... Batch 6000/6331... Discriminator loss: 1.3785 Generator loss: 0.7679
Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [13:48<00:46,  7.18batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.4068 Generator loss: 0.7537
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3762 Generator loss: 0.8144
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3948 Generator loss: 0.7807
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.3548 Generator loss: 0.7948
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3860 Generator loss: 0.7819
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.3855 Generator loss: 0.8065
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.4001 Generator loss: 0.7622
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.3965 Generator loss: 0.7953
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.3846 Generator loss: 0.7916
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3713 Generator loss: 0.7928
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Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.3917 Generator loss: 0.7902
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.3744 Generator loss: 0.7725
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Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3763 Generator loss: 0.7945
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Epoch 0/1... Batch 6210/6331... Discriminator loss: 1.3952 Generator loss: 0.7737
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Batches: 100%|███████████████████████████████████████████████████████████████████████▋| 6300/6331 [14:29<00:04,  7.26batch/s]
Epoch 0/1... Batch 6310/6331... Discriminator loss: 1.3937 Generator loss: 0.7800
Epoch 0/1... Batch 6320/6331... Discriminator loss: 1.4001 Generator loss: 0.7702
Epoch 0/1... Batch 6330/6331... Discriminator loss: 1.3858 Generator loss: 0.7886
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [14:33<00:00, 873.21s/epoch]
In [22]:
batch_size = 32
z_dim = 200
learning_rate = 0.002
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 3.0599 Generator loss: 0.2022
Epoch 0/1... Batch 20/6331... Discriminator loss: 1.6020 Generator loss: 1.0375
Epoch 0/1... Batch 30/6331... Discriminator loss: 2.0318 Generator loss: 0.3676
Epoch 0/1... Batch 40/6331... Discriminator loss: 2.2556 Generator loss: 3.6343
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.2799 Generator loss: 0.8415
Epoch 0/1... Batch 60/6331... Discriminator loss: 1.4532 Generator loss: 3.6584
Epoch 0/1... Batch 70/6331... Discriminator loss: 1.1448 Generator loss: 1.1149
Epoch 0/1... Batch 80/6331... Discriminator loss: 4.1130 Generator loss: 1.9821
Epoch 0/1... Batch 90/6331... Discriminator loss: 1.7820 Generator loss: 0.5212
Epoch 0/1... Batch 100/6331... Discriminator loss: 1.3914 Generator loss: 0.9032
Batches:   2%|█▏                                                                       | 100/6331 [00:20<21:13,  4.89batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 2.1552 Generator loss: 0.6223
Epoch 0/1... Batch 120/6331... Discriminator loss: 1.6587 Generator loss: 0.4743
Epoch 0/1... Batch 130/6331... Discriminator loss: 2.2416 Generator loss: 0.2690
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.8161 Generator loss: 0.4261
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.8663 Generator loss: 1.2373
Epoch 0/1... Batch 160/6331... Discriminator loss: 1.7989 Generator loss: 0.5121
Epoch 0/1... Batch 170/6331... Discriminator loss: 2.2521 Generator loss: 0.6004
Epoch 0/1... Batch 180/6331... Discriminator loss: 1.8970 Generator loss: 0.6565
Epoch 0/1... Batch 190/6331... Discriminator loss: 1.6649 Generator loss: 0.5746
Epoch 0/1... Batch 200/6331... Discriminator loss: 3.0268 Generator loss: 0.2713
Batches:   3%|██▎                                                                      | 200/6331 [00:40<20:38,  4.95batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.4654 Generator loss: 1.1791
Epoch 0/1... Batch 220/6331... Discriminator loss: 1.8661 Generator loss: 1.2836
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.5679 Generator loss: 0.5172
Epoch 0/1... Batch 240/6331... Discriminator loss: 1.6973 Generator loss: 0.6153
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.6238 Generator loss: 0.9990
Epoch 0/1... Batch 260/6331... Discriminator loss: 1.6379 Generator loss: 1.0291
Epoch 0/1... Batch 270/6331... Discriminator loss: 1.9137 Generator loss: 0.4852
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.7044 Generator loss: 0.7043
Epoch 0/1... Batch 290/6331... Discriminator loss: 1.6377 Generator loss: 0.7749
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.5941 Generator loss: 0.6610
Batches:   5%|███▍                                                                     | 300/6331 [01:01<20:37,  4.87batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.8440 Generator loss: 0.6588
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.7761 Generator loss: 0.5778
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.6315 Generator loss: 0.6715
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.8651 Generator loss: 1.1667
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.7157 Generator loss: 0.3781
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.4067 Generator loss: 0.8118
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.9382 Generator loss: 0.5852
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.3596 Generator loss: 0.8081
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.5816 Generator loss: 0.6382
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.5895 Generator loss: 0.7250
Batches:   6%|████▌                                                                    | 400/6331 [01:21<20:03,  4.93batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.5304 Generator loss: 0.7694
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.6198 Generator loss: 0.6161
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.5929 Generator loss: 0.9247
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.5276 Generator loss: 0.6868
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.6165 Generator loss: 0.6713
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.4023 Generator loss: 0.9631
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.5503 Generator loss: 0.5960
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.4513 Generator loss: 0.7274
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.4405 Generator loss: 0.7560
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.4869 Generator loss: 0.7936
Batches:   8%|█████▊                                                                   | 500/6331 [01:41<19:43,  4.93batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.4750 Generator loss: 0.8107
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.5162 Generator loss: 0.6421
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.6059 Generator loss: 0.5170
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.5322 Generator loss: 0.6412
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.5242 Generator loss: 0.8479
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.6130 Generator loss: 0.4527
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.5566 Generator loss: 0.6003
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.5253 Generator loss: 0.6473
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.6102 Generator loss: 0.4875
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.5015 Generator loss: 0.6794
Batches:   9%|██████▉                                                                  | 600/6331 [02:02<19:37,  4.87batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.4789 Generator loss: 0.6402
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.5491 Generator loss: 0.6087
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.5298 Generator loss: 0.8812
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.6168 Generator loss: 0.6849
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.4269 Generator loss: 0.7742
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.6240 Generator loss: 0.7434
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.4024 Generator loss: 0.8158
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.6558 Generator loss: 0.6225
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.4859 Generator loss: 0.8364
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.4797 Generator loss: 0.9222
Batches:  11%|████████                                                                 | 700/6331 [02:21<18:56,  4.95batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.4854 Generator loss: 0.8272
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.4118 Generator loss: 0.7427
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.4807 Generator loss: 0.6613
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.4890 Generator loss: 0.6445
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.5758 Generator loss: 0.8956
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.4733 Generator loss: 0.6545
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.4984 Generator loss: 0.7210
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.5842 Generator loss: 0.7440
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.4774 Generator loss: 0.6900
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.5635 Generator loss: 0.8077
Batches:  13%|█████████▏                                                               | 800/6331 [02:42<18:43,  4.92batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.3670 Generator loss: 0.7589
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.7766 Generator loss: 0.6751
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.4664 Generator loss: 0.7101
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.4340 Generator loss: 0.7340
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.4265 Generator loss: 1.0342
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.4741 Generator loss: 0.7583
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.6285 Generator loss: 0.6310
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.3650 Generator loss: 0.8752
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.5924 Generator loss: 0.6408
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.4855 Generator loss: 0.5941
Batches:  14%|██████████▍                                                              | 900/6331 [03:02<18:17,  4.95batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.5339 Generator loss: 0.6931
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.5676 Generator loss: 0.7484
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.4710 Generator loss: 0.7317
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.4289 Generator loss: 0.6969
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.4728 Generator loss: 0.7284
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.4182 Generator loss: 0.9641
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.4948 Generator loss: 0.5590
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.5037 Generator loss: 0.7417
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.5329 Generator loss: 0.5618
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.4626 Generator loss: 0.7748
Batches:  16%|███████████▎                                                            | 1000/6331 [03:23<18:18,  4.85batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.4508 Generator loss: 0.7520
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.4197 Generator loss: 0.7421
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.4563 Generator loss: 0.7178
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.5546 Generator loss: 0.6778
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.3906 Generator loss: 0.8115
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.4376 Generator loss: 0.6683
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.4643 Generator loss: 1.0243
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.4300 Generator loss: 0.6791
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.4539 Generator loss: 0.8585
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.4216 Generator loss: 0.6680
Batches:  17%|████████████▌                                                           | 1100/6331 [03:43<17:48,  4.90batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.4546 Generator loss: 0.7391
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.5588 Generator loss: 0.7175
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.4761 Generator loss: 0.6682
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.6582 Generator loss: 0.5650
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.6194 Generator loss: 0.6456
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.6419 Generator loss: 0.5675
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.4732 Generator loss: 0.5331
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.6085 Generator loss: 0.6922
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.3875 Generator loss: 0.7699
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.3963 Generator loss: 0.8275
Batches:  19%|█████████████▋                                                          | 1200/6331 [04:03<17:17,  4.95batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.3137 Generator loss: 0.8322
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.4234 Generator loss: 0.8287
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.5179 Generator loss: 0.7934
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.5373 Generator loss: 0.5216
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.4631 Generator loss: 0.6278
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.5032 Generator loss: 0.7294
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.3856 Generator loss: 0.7072
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.4806 Generator loss: 0.6973
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.4608 Generator loss: 0.9615
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.4481 Generator loss: 0.7602
Batches:  21%|██████████████▊                                                         | 1300/6331 [04:23<16:51,  4.97batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.4883 Generator loss: 0.8028
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.3658 Generator loss: 0.8867
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.5346 Generator loss: 0.7422
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.3881 Generator loss: 0.8343
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.5551 Generator loss: 0.8093
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.4831 Generator loss: 0.7752
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.4447 Generator loss: 0.7330
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.4365 Generator loss: 0.6601
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.3777 Generator loss: 0.8351
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.4637 Generator loss: 0.7039
Batches:  22%|███████████████▉                                                        | 1400/6331 [04:44<16:47,  4.90batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.4217 Generator loss: 0.7377
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.4660 Generator loss: 1.0586
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.4512 Generator loss: 0.6952
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.4858 Generator loss: 0.6966
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4459 Generator loss: 0.7781
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.4416 Generator loss: 0.8097
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.3844 Generator loss: 0.8983
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.4535 Generator loss: 0.9788
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4260 Generator loss: 0.6081
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.3890 Generator loss: 0.7938
Batches:  24%|█████████████████                                                       | 1500/6331 [05:05<16:29,  4.88batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.4058 Generator loss: 0.8011
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.4293 Generator loss: 0.6647
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.4402 Generator loss: 0.6589
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.4017 Generator loss: 0.8244
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.3985 Generator loss: 0.7907
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.4538 Generator loss: 0.7162
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.4050 Generator loss: 0.9125
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.4541 Generator loss: 0.7296
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.4332 Generator loss: 0.8114
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.5108 Generator loss: 0.8024
Batches:  25%|██████████████████▏                                                     | 1600/6331 [05:25<15:59,  4.93batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.4155 Generator loss: 0.7314
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.3573 Generator loss: 0.7409
Epoch 0/1... Batch 1630/6331... Discriminator loss: 1.4487 Generator loss: 0.7333
Epoch 0/1... Batch 1640/6331... Discriminator loss: 1.4359 Generator loss: 0.8847
Epoch 0/1... Batch 1650/6331... Discriminator loss: 1.4043 Generator loss: 0.8067
Epoch 0/1... Batch 1660/6331... Discriminator loss: 1.4153 Generator loss: 0.9421
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Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.4811 Generator loss: 0.7732
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Batches:  28%|████████████████████▍                                                   | 1800/6331 [06:05<15:21,  4.92batch/s]
Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.4570 Generator loss: 0.8149
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Batches:  30%|█████████████████████▌                                                  | 1900/6331 [06:25<14:46,  5.00batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.4859 Generator loss: 0.8288
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Batches:  32%|██████████████████████▋                                                 | 2000/6331 [06:45<14:33,  4.96batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.4225 Generator loss: 0.7802
Epoch 0/1... Batch 2020/6331... Discriminator loss: 1.4615 Generator loss: 0.6534
Epoch 0/1... Batch 2030/6331... Discriminator loss: 1.4827 Generator loss: 0.6359
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Epoch 0/1... Batch 2050/6331... Discriminator loss: 1.5201 Generator loss: 0.6739
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Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.4619 Generator loss: 0.8826
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Batches:  33%|███████████████████████▉                                                | 2100/6331 [07:09<14:55,  4.72batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.4574 Generator loss: 0.7029
Epoch 0/1... Batch 2120/6331... Discriminator loss: 1.4744 Generator loss: 0.6610
Epoch 0/1... Batch 2130/6331... Discriminator loss: 1.4181 Generator loss: 0.6739
Epoch 0/1... Batch 2140/6331... Discriminator loss: 1.4346 Generator loss: 0.7795
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Epoch 0/1... Batch 2160/6331... Discriminator loss: 1.4399 Generator loss: 0.6822
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Epoch 0/1... Batch 2200/6331... Discriminator loss: 1.3847 Generator loss: 0.8076
Batches:  35%|█████████████████████████                                               | 2200/6331 [07:29<14:24,  4.78batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.4028 Generator loss: 0.8563
Epoch 0/1... Batch 2220/6331... Discriminator loss: 1.3907 Generator loss: 0.8895
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Epoch 0/1... Batch 2270/6331... Discriminator loss: 1.3766 Generator loss: 0.8374
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Epoch 0/1... Batch 2300/6331... Discriminator loss: 1.4334 Generator loss: 0.7077
Batches:  36%|██████████████████████████▏                                             | 2300/6331 [07:52<14:24,  4.66batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.4016 Generator loss: 0.9586
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.4653 Generator loss: 0.8232
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.4489 Generator loss: 0.6521
Epoch 0/1... Batch 2340/6331... Discriminator loss: 1.4982 Generator loss: 0.7542
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Batches:  38%|███████████████████████████▎                                            | 2400/6331 [08:12<13:53,  4.72batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.4276 Generator loss: 0.7805
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.4326 Generator loss: 0.7606
Epoch 0/1... Batch 2430/6331... Discriminator loss: 1.3857 Generator loss: 0.7989
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Epoch 0/1... Batch 2500/6331... Discriminator loss: 1.4092 Generator loss: 0.7083
Batches:  39%|████████████████████████████▍                                           | 2500/6331 [08:31<13:02,  4.89batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.3762 Generator loss: 0.9032
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Epoch 0/1... Batch 2530/6331... Discriminator loss: 1.3834 Generator loss: 0.7491
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Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [08:51<12:38,  4.92batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.4262 Generator loss: 0.6658
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Epoch 0/1... Batch 2660/6331... Discriminator loss: 1.4110 Generator loss: 0.7149
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Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [09:11<12:10,  4.97batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.3765 Generator loss: 0.8209
Epoch 0/1... Batch 2720/6331... Discriminator loss: 1.4097 Generator loss: 0.7087
Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.4113 Generator loss: 0.7192
Epoch 0/1... Batch 2740/6331... Discriminator loss: 1.4960 Generator loss: 0.6508
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Epoch 0/1... Batch 2760/6331... Discriminator loss: 1.4286 Generator loss: 0.8354
Epoch 0/1... Batch 2770/6331... Discriminator loss: 1.4049 Generator loss: 0.7019
Epoch 0/1... Batch 2780/6331... Discriminator loss: 1.3964 Generator loss: 0.7261
Epoch 0/1... Batch 2790/6331... Discriminator loss: 1.3629 Generator loss: 0.8064
Epoch 0/1... Batch 2800/6331... Discriminator loss: 1.4145 Generator loss: 0.8121
Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [09:29<11:36,  5.07batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.3774 Generator loss: 0.8414
Epoch 0/1... Batch 2820/6331... Discriminator loss: 1.3967 Generator loss: 0.9030
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Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [09:48<11:06,  5.15batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4390 Generator loss: 0.8836
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Batches:  47%|██████████████████████████████████                                      | 3000/6331 [10:08<10:47,  5.15batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.3930 Generator loss: 0.7773
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [10:27<10:26,  5.16batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.3834 Generator loss: 0.8017
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [10:47<10:13,  5.11batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4689 Generator loss: 0.6781
Epoch 0/1... Batch 3220/6331... Discriminator loss: 1.4418 Generator loss: 0.7472
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [11:06<09:50,  5.13batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.3774 Generator loss: 0.8538
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [11:26<09:34,  5.10batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.3913 Generator loss: 0.7360
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [11:46<09:14,  5.10batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.4212 Generator loss: 0.7484
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [12:05<08:52,  5.12batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.4255 Generator loss: 0.7892
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Epoch 0/1... Batch 5870/6331... Discriminator loss: 1.3854 Generator loss: 0.7814
Epoch 0/1... Batch 5880/6331... Discriminator loss: 1.3853 Generator loss: 0.7924
Epoch 0/1... Batch 5890/6331... Discriminator loss: 1.3893 Generator loss: 0.7769
Epoch 0/1... Batch 5900/6331... Discriminator loss: 1.3722 Generator loss: 0.8090
Batches:  93%|███████████████████████████████████████████████████████████████████     | 5900/6331 [19:21<01:02,  6.84batch/s]
Epoch 0/1... Batch 5910/6331... Discriminator loss: 1.3890 Generator loss: 0.7774
Epoch 0/1... Batch 5920/6331... Discriminator loss: 1.3804 Generator loss: 0.7872
Epoch 0/1... Batch 5930/6331... Discriminator loss: 1.3934 Generator loss: 0.8175
Epoch 0/1... Batch 5940/6331... Discriminator loss: 1.3811 Generator loss: 0.7973
Epoch 0/1... Batch 5950/6331... Discriminator loss: 1.3854 Generator loss: 0.7754
Epoch 0/1... Batch 5960/6331... Discriminator loss: 1.3958 Generator loss: 0.7982
Epoch 0/1... Batch 5970/6331... Discriminator loss: 1.3913 Generator loss: 0.7789
Epoch 0/1... Batch 5980/6331... Discriminator loss: 1.3864 Generator loss: 0.7614
Epoch 0/1... Batch 5990/6331... Discriminator loss: 1.3873 Generator loss: 0.7890
Epoch 0/1... Batch 6000/6331... Discriminator loss: 1.3866 Generator loss: 0.8133
Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [19:34<00:47,  6.99batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.3938 Generator loss: 0.7680
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3823 Generator loss: 0.7899
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3807 Generator loss: 0.7815
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.3940 Generator loss: 0.7829
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3884 Generator loss: 0.7680
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.3953 Generator loss: 0.7765
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.3813 Generator loss: 0.7832
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.3936 Generator loss: 0.7680
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.3968 Generator loss: 0.7422
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3861 Generator loss: 0.7908
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Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.3862 Generator loss: 0.7712
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.3866 Generator loss: 0.7842
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Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3828 Generator loss: 0.8029
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Epoch 0/1... Batch 6210/6331... Discriminator loss: 1.3912 Generator loss: 0.7896
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Epoch 0/1... Batch 6310/6331... Discriminator loss: 1.3794 Generator loss: 0.7933
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Epochs: 100%|█████████████████████████████████████████████████████████████████████████████| 1/1 [20:20<00:00, 1220.43s/epoch]
In [37]:
batch_size = 32
z_dim = 200
learning_rate = 0.002
beta1 = 0.3
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
Epochs:   0%|                                                                                       | 0/1 [00:00<?, ?epoch/s]
Batches:   0%|                                                                                   | 0/6331 [00:00<?, ?batch/s]
Epoch 0/1... Batch 10/6331... Discriminator loss: 3.9754 Generator loss: 0.1650
Epoch 0/1... Batch 20/6331... Discriminator loss: 2.0891 Generator loss: 0.2722
Epoch 0/1... Batch 30/6331... Discriminator loss: 1.5292 Generator loss: 1.1602
Epoch 0/1... Batch 40/6331... Discriminator loss: 1.3910 Generator loss: 1.4078
Epoch 0/1... Batch 50/6331... Discriminator loss: 1.5541 Generator loss: 0.6097
Epoch 0/1... Batch 60/6331... Discriminator loss: 3.4607 Generator loss: 2.9490
Epoch 0/1... Batch 70/6331... Discriminator loss: 1.4156 Generator loss: 1.1056
Epoch 0/1... Batch 80/6331... Discriminator loss: 1.3131 Generator loss: 1.4627
Epoch 0/1... Batch 90/6331... Discriminator loss: 1.3870 Generator loss: 0.8783
Epoch 0/1... Batch 100/6331... Discriminator loss: 1.4284 Generator loss: 0.5692
Batches:   2%|█▏                                                                       | 100/6331 [00:13<14:18,  7.26batch/s]
Epoch 0/1... Batch 110/6331... Discriminator loss: 2.3961 Generator loss: 0.3325
Epoch 0/1... Batch 120/6331... Discriminator loss: 1.6608 Generator loss: 0.6194
Epoch 0/1... Batch 130/6331... Discriminator loss: 1.7591 Generator loss: 0.6958
Epoch 0/1... Batch 140/6331... Discriminator loss: 1.5826 Generator loss: 0.5292
Epoch 0/1... Batch 150/6331... Discriminator loss: 1.5203 Generator loss: 0.6302
Epoch 0/1... Batch 160/6331... Discriminator loss: 1.6977 Generator loss: 0.5293
Epoch 0/1... Batch 170/6331... Discriminator loss: 2.3793 Generator loss: 0.8690
Epoch 0/1... Batch 180/6331... Discriminator loss: 1.4595 Generator loss: 0.5302
Epoch 0/1... Batch 190/6331... Discriminator loss: 2.3990 Generator loss: 0.2592
Epoch 0/1... Batch 200/6331... Discriminator loss: 1.4603 Generator loss: 0.8557
Batches:   3%|██▎                                                                      | 200/6331 [00:27<13:56,  7.33batch/s]
Epoch 0/1... Batch 210/6331... Discriminator loss: 1.6008 Generator loss: 0.7085
Epoch 0/1... Batch 220/6331... Discriminator loss: 1.5260 Generator loss: 0.7477
Epoch 0/1... Batch 230/6331... Discriminator loss: 1.4714 Generator loss: 0.8830
Epoch 0/1... Batch 240/6331... Discriminator loss: 1.5238 Generator loss: 0.6388
Epoch 0/1... Batch 250/6331... Discriminator loss: 1.5442 Generator loss: 0.6673
Epoch 0/1... Batch 260/6331... Discriminator loss: 1.7661 Generator loss: 0.5013
Epoch 0/1... Batch 270/6331... Discriminator loss: 1.4713 Generator loss: 0.9840
Epoch 0/1... Batch 280/6331... Discriminator loss: 1.4686 Generator loss: 0.6756
Epoch 0/1... Batch 290/6331... Discriminator loss: 1.6939 Generator loss: 0.6037
Epoch 0/1... Batch 300/6331... Discriminator loss: 1.3958 Generator loss: 0.8836
Batches:   5%|███▍                                                                     | 300/6331 [00:41<14:01,  7.16batch/s]
Epoch 0/1... Batch 310/6331... Discriminator loss: 1.5609 Generator loss: 0.5396
Epoch 0/1... Batch 320/6331... Discriminator loss: 1.4529 Generator loss: 0.6442
Epoch 0/1... Batch 330/6331... Discriminator loss: 1.6195 Generator loss: 0.7108
Epoch 0/1... Batch 340/6331... Discriminator loss: 1.4886 Generator loss: 0.7618
Epoch 0/1... Batch 350/6331... Discriminator loss: 1.5131 Generator loss: 0.6900
Epoch 0/1... Batch 360/6331... Discriminator loss: 1.4492 Generator loss: 0.6242
Epoch 0/1... Batch 370/6331... Discriminator loss: 1.4766 Generator loss: 0.7120
Epoch 0/1... Batch 380/6331... Discriminator loss: 1.5052 Generator loss: 0.7291
Epoch 0/1... Batch 390/6331... Discriminator loss: 1.4652 Generator loss: 0.7663
Epoch 0/1... Batch 400/6331... Discriminator loss: 1.4861 Generator loss: 0.7472
Batches:   6%|████▌                                                                    | 400/6331 [00:57<14:17,  6.92batch/s]
Epoch 0/1... Batch 410/6331... Discriminator loss: 1.5733 Generator loss: 0.6276
Epoch 0/1... Batch 420/6331... Discriminator loss: 1.6487 Generator loss: 0.5782
Epoch 0/1... Batch 430/6331... Discriminator loss: 1.4759 Generator loss: 0.6065
Epoch 0/1... Batch 440/6331... Discriminator loss: 1.4839 Generator loss: 0.6674
Epoch 0/1... Batch 450/6331... Discriminator loss: 1.5895 Generator loss: 0.7082
Epoch 0/1... Batch 460/6331... Discriminator loss: 1.5227 Generator loss: 0.7739
Epoch 0/1... Batch 470/6331... Discriminator loss: 1.6212 Generator loss: 0.4643
Epoch 0/1... Batch 480/6331... Discriminator loss: 1.4399 Generator loss: 1.0099
Epoch 0/1... Batch 490/6331... Discriminator loss: 1.5532 Generator loss: 0.6102
Epoch 0/1... Batch 500/6331... Discriminator loss: 1.4458 Generator loss: 0.7662
Batches:   8%|█████▊                                                                   | 500/6331 [01:11<13:53,  6.99batch/s]
Epoch 0/1... Batch 510/6331... Discriminator loss: 1.4579 Generator loss: 0.8043
Epoch 0/1... Batch 520/6331... Discriminator loss: 1.6286 Generator loss: 0.8256
Epoch 0/1... Batch 530/6331... Discriminator loss: 1.5472 Generator loss: 0.5692
Epoch 0/1... Batch 540/6331... Discriminator loss: 1.6379 Generator loss: 0.5994
Epoch 0/1... Batch 550/6331... Discriminator loss: 1.4308 Generator loss: 0.7738
Epoch 0/1... Batch 560/6331... Discriminator loss: 1.6003 Generator loss: 0.4447
Epoch 0/1... Batch 570/6331... Discriminator loss: 1.5651 Generator loss: 0.5710
Epoch 0/1... Batch 580/6331... Discriminator loss: 1.4256 Generator loss: 0.8987
Epoch 0/1... Batch 590/6331... Discriminator loss: 1.6066 Generator loss: 0.5654
Epoch 0/1... Batch 600/6331... Discriminator loss: 1.4569 Generator loss: 0.8322
Batches:   9%|██████▉                                                                  | 600/6331 [01:26<13:55,  6.86batch/s]
Epoch 0/1... Batch 610/6331... Discriminator loss: 1.4783 Generator loss: 0.7019
Epoch 0/1... Batch 620/6331... Discriminator loss: 1.5133 Generator loss: 0.7840
Epoch 0/1... Batch 630/6331... Discriminator loss: 1.5400 Generator loss: 0.7313
Epoch 0/1... Batch 640/6331... Discriminator loss: 1.5880 Generator loss: 0.5531
Epoch 0/1... Batch 650/6331... Discriminator loss: 1.5843 Generator loss: 0.6979
Epoch 0/1... Batch 660/6331... Discriminator loss: 1.5723 Generator loss: 0.6880
Epoch 0/1... Batch 670/6331... Discriminator loss: 1.5365 Generator loss: 0.8244
Epoch 0/1... Batch 680/6331... Discriminator loss: 1.5619 Generator loss: 0.6707
Epoch 0/1... Batch 690/6331... Discriminator loss: 1.4949 Generator loss: 0.9527
Epoch 0/1... Batch 700/6331... Discriminator loss: 1.4655 Generator loss: 0.6108
Batches:  11%|████████                                                                 | 700/6331 [01:40<13:31,  6.94batch/s]
Epoch 0/1... Batch 710/6331... Discriminator loss: 1.4240 Generator loss: 0.6809
Epoch 0/1... Batch 720/6331... Discriminator loss: 1.3980 Generator loss: 0.8277
Epoch 0/1... Batch 730/6331... Discriminator loss: 1.5432 Generator loss: 0.5988
Epoch 0/1... Batch 740/6331... Discriminator loss: 1.4805 Generator loss: 0.6282
Epoch 0/1... Batch 750/6331... Discriminator loss: 1.4803 Generator loss: 0.7744
Epoch 0/1... Batch 760/6331... Discriminator loss: 1.4967 Generator loss: 0.7478
Epoch 0/1... Batch 770/6331... Discriminator loss: 1.4164 Generator loss: 0.8177
Epoch 0/1... Batch 780/6331... Discriminator loss: 1.4593 Generator loss: 0.6113
Epoch 0/1... Batch 790/6331... Discriminator loss: 1.4564 Generator loss: 0.9003
Epoch 0/1... Batch 800/6331... Discriminator loss: 1.4095 Generator loss: 0.7620
Batches:  13%|█████████▏                                                               | 800/6331 [01:56<13:46,  6.70batch/s]
Epoch 0/1... Batch 810/6331... Discriminator loss: 1.4888 Generator loss: 0.8335
Epoch 0/1... Batch 820/6331... Discriminator loss: 1.4658 Generator loss: 0.8376
Epoch 0/1... Batch 830/6331... Discriminator loss: 1.5773 Generator loss: 0.6871
Epoch 0/1... Batch 840/6331... Discriminator loss: 1.4586 Generator loss: 0.7980
Epoch 0/1... Batch 850/6331... Discriminator loss: 1.4445 Generator loss: 0.7880
Epoch 0/1... Batch 860/6331... Discriminator loss: 1.4176 Generator loss: 0.8421
Epoch 0/1... Batch 870/6331... Discriminator loss: 1.4576 Generator loss: 0.6995
Epoch 0/1... Batch 880/6331... Discriminator loss: 1.4885 Generator loss: 1.0701
Epoch 0/1... Batch 890/6331... Discriminator loss: 1.3957 Generator loss: 0.6886
Epoch 0/1... Batch 900/6331... Discriminator loss: 1.4388 Generator loss: 0.6443
Batches:  14%|██████████▍                                                              | 900/6331 [02:11<13:20,  6.79batch/s]
Epoch 0/1... Batch 910/6331... Discriminator loss: 1.4323 Generator loss: 0.8994
Epoch 0/1... Batch 920/6331... Discriminator loss: 1.5955 Generator loss: 0.6463
Epoch 0/1... Batch 930/6331... Discriminator loss: 1.4374 Generator loss: 0.7459
Epoch 0/1... Batch 940/6331... Discriminator loss: 1.4850 Generator loss: 0.6658
Epoch 0/1... Batch 950/6331... Discriminator loss: 1.4522 Generator loss: 0.7066
Epoch 0/1... Batch 960/6331... Discriminator loss: 1.4497 Generator loss: 0.7206
Epoch 0/1... Batch 970/6331... Discriminator loss: 1.4390 Generator loss: 0.8370
Epoch 0/1... Batch 980/6331... Discriminator loss: 1.4078 Generator loss: 0.7323
Epoch 0/1... Batch 990/6331... Discriminator loss: 1.4531 Generator loss: 0.7504
Epoch 0/1... Batch 1000/6331... Discriminator loss: 1.4498 Generator loss: 0.6883
Batches:  16%|███████████▎                                                            | 1000/6331 [02:26<13:20,  6.66batch/s]
Epoch 0/1... Batch 1010/6331... Discriminator loss: 1.4094 Generator loss: 0.7986
Epoch 0/1... Batch 1020/6331... Discriminator loss: 1.4616 Generator loss: 0.8185
Epoch 0/1... Batch 1030/6331... Discriminator loss: 1.4508 Generator loss: 0.8719
Epoch 0/1... Batch 1040/6331... Discriminator loss: 1.4524 Generator loss: 0.6524
Epoch 0/1... Batch 1050/6331... Discriminator loss: 1.5423 Generator loss: 0.6138
Epoch 0/1... Batch 1060/6331... Discriminator loss: 1.4089 Generator loss: 0.7123
Epoch 0/1... Batch 1070/6331... Discriminator loss: 1.4552 Generator loss: 0.8124
Epoch 0/1... Batch 1080/6331... Discriminator loss: 1.4585 Generator loss: 0.8063
Epoch 0/1... Batch 1090/6331... Discriminator loss: 1.4089 Generator loss: 1.0119
Epoch 0/1... Batch 1100/6331... Discriminator loss: 1.4624 Generator loss: 0.6566
Batches:  17%|████████████▌                                                           | 1100/6331 [02:41<13:02,  6.68batch/s]
Epoch 0/1... Batch 1110/6331... Discriminator loss: 1.5121 Generator loss: 0.7988
Epoch 0/1... Batch 1120/6331... Discriminator loss: 1.4966 Generator loss: 0.6060
Epoch 0/1... Batch 1130/6331... Discriminator loss: 1.4115 Generator loss: 0.7373
Epoch 0/1... Batch 1140/6331... Discriminator loss: 1.4805 Generator loss: 0.7679
Epoch 0/1... Batch 1150/6331... Discriminator loss: 1.4450 Generator loss: 0.6774
Epoch 0/1... Batch 1160/6331... Discriminator loss: 1.4470 Generator loss: 0.7149
Epoch 0/1... Batch 1170/6331... Discriminator loss: 1.4187 Generator loss: 0.6977
Epoch 0/1... Batch 1180/6331... Discriminator loss: 1.4831 Generator loss: 0.8607
Epoch 0/1... Batch 1190/6331... Discriminator loss: 1.3736 Generator loss: 0.7208
Epoch 0/1... Batch 1200/6331... Discriminator loss: 1.4028 Generator loss: 0.8438
Batches:  19%|█████████████▋                                                          | 1200/6331 [02:55<12:34,  6.80batch/s]
Epoch 0/1... Batch 1210/6331... Discriminator loss: 1.4382 Generator loss: 0.8536
Epoch 0/1... Batch 1220/6331... Discriminator loss: 1.4058 Generator loss: 0.8088
Epoch 0/1... Batch 1230/6331... Discriminator loss: 1.4210 Generator loss: 0.7770
Epoch 0/1... Batch 1240/6331... Discriminator loss: 1.4444 Generator loss: 0.6070
Epoch 0/1... Batch 1250/6331... Discriminator loss: 1.4306 Generator loss: 0.6926
Epoch 0/1... Batch 1260/6331... Discriminator loss: 1.4287 Generator loss: 0.8467
Epoch 0/1... Batch 1270/6331... Discriminator loss: 1.5123 Generator loss: 0.5745
Epoch 0/1... Batch 1280/6331... Discriminator loss: 1.4634 Generator loss: 0.7299
Epoch 0/1... Batch 1290/6331... Discriminator loss: 1.5104 Generator loss: 0.6694
Epoch 0/1... Batch 1300/6331... Discriminator loss: 1.4335 Generator loss: 0.8640
Batches:  21%|██████████████▊                                                         | 1300/6331 [03:09<12:04,  6.94batch/s]
Epoch 0/1... Batch 1310/6331... Discriminator loss: 1.4249 Generator loss: 0.6445
Epoch 0/1... Batch 1320/6331... Discriminator loss: 1.3841 Generator loss: 0.7935
Epoch 0/1... Batch 1330/6331... Discriminator loss: 1.4369 Generator loss: 0.8937
Epoch 0/1... Batch 1340/6331... Discriminator loss: 1.4436 Generator loss: 0.7032
Epoch 0/1... Batch 1350/6331... Discriminator loss: 1.4549 Generator loss: 0.6544
Epoch 0/1... Batch 1360/6331... Discriminator loss: 1.4268 Generator loss: 0.6293
Epoch 0/1... Batch 1370/6331... Discriminator loss: 1.4258 Generator loss: 0.6665
Epoch 0/1... Batch 1380/6331... Discriminator loss: 1.4558 Generator loss: 0.7253
Epoch 0/1... Batch 1390/6331... Discriminator loss: 1.4888 Generator loss: 0.5841
Epoch 0/1... Batch 1400/6331... Discriminator loss: 1.4582 Generator loss: 0.7633
Batches:  22%|███████████████▉                                                        | 1400/6331 [03:23<11:51,  6.93batch/s]
Epoch 0/1... Batch 1410/6331... Discriminator loss: 1.4097 Generator loss: 0.7344
Epoch 0/1... Batch 1420/6331... Discriminator loss: 1.4499 Generator loss: 0.7576
Epoch 0/1... Batch 1430/6331... Discriminator loss: 1.3975 Generator loss: 0.7852
Epoch 0/1... Batch 1440/6331... Discriminator loss: 1.4814 Generator loss: 0.8245
Epoch 0/1... Batch 1450/6331... Discriminator loss: 1.4057 Generator loss: 0.7270
Epoch 0/1... Batch 1460/6331... Discriminator loss: 1.4063 Generator loss: 0.9962
Epoch 0/1... Batch 1470/6331... Discriminator loss: 1.4296 Generator loss: 0.7834
Epoch 0/1... Batch 1480/6331... Discriminator loss: 1.4466 Generator loss: 0.7031
Epoch 0/1... Batch 1490/6331... Discriminator loss: 1.4082 Generator loss: 0.7299
Epoch 0/1... Batch 1500/6331... Discriminator loss: 1.4575 Generator loss: 0.7664
Batches:  24%|█████████████████                                                       | 1500/6331 [03:37<11:26,  7.04batch/s]
Epoch 0/1... Batch 1510/6331... Discriminator loss: 1.4230 Generator loss: 0.7251
Epoch 0/1... Batch 1520/6331... Discriminator loss: 1.4347 Generator loss: 0.7412
Epoch 0/1... Batch 1530/6331... Discriminator loss: 1.4359 Generator loss: 0.7128
Epoch 0/1... Batch 1540/6331... Discriminator loss: 1.4319 Generator loss: 0.8533
Epoch 0/1... Batch 1550/6331... Discriminator loss: 1.4524 Generator loss: 0.9416
Epoch 0/1... Batch 1560/6331... Discriminator loss: 1.4229 Generator loss: 0.7653
Epoch 0/1... Batch 1570/6331... Discriminator loss: 1.3923 Generator loss: 0.8002
Epoch 0/1... Batch 1580/6331... Discriminator loss: 1.4069 Generator loss: 0.8185
Epoch 0/1... Batch 1590/6331... Discriminator loss: 1.4054 Generator loss: 0.7560
Epoch 0/1... Batch 1600/6331... Discriminator loss: 1.4513 Generator loss: 0.9158
Batches:  25%|██████████████████▏                                                     | 1600/6331 [03:51<11:02,  7.14batch/s]
Epoch 0/1... Batch 1610/6331... Discriminator loss: 1.4783 Generator loss: 0.8811
Epoch 0/1... Batch 1620/6331... Discriminator loss: 1.3983 Generator loss: 0.8104
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Epoch 0/1... Batch 1710/6331... Discriminator loss: 1.4100 Generator loss: 0.7428
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Epoch 0/1... Batch 1800/6331... Discriminator loss: 1.4026 Generator loss: 0.7537
Batches:  28%|████████████████████▍                                                   | 1800/6331 [04:18<10:31,  7.17batch/s]
Epoch 0/1... Batch 1810/6331... Discriminator loss: 1.4105 Generator loss: 0.7725
Epoch 0/1... Batch 1820/6331... Discriminator loss: 1.3738 Generator loss: 0.7116
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Batches:  30%|█████████████████████▌                                                  | 1900/6331 [04:33<10:19,  7.15batch/s]
Epoch 0/1... Batch 1910/6331... Discriminator loss: 1.4143 Generator loss: 0.9446
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Epoch 0/1... Batch 2000/6331... Discriminator loss: 1.4322 Generator loss: 0.8317
Batches:  32%|██████████████████████▋                                                 | 2000/6331 [04:46<10:00,  7.21batch/s]
Epoch 0/1... Batch 2010/6331... Discriminator loss: 1.5120 Generator loss: 0.7565
Epoch 0/1... Batch 2020/6331... Discriminator loss: 1.4621 Generator loss: 0.8208
Epoch 0/1... Batch 2030/6331... Discriminator loss: 1.4753 Generator loss: 0.6436
Epoch 0/1... Batch 2040/6331... Discriminator loss: 1.4390 Generator loss: 0.7907
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Epoch 0/1... Batch 2060/6331... Discriminator loss: 1.4135 Generator loss: 0.6715
Epoch 0/1... Batch 2070/6331... Discriminator loss: 1.4380 Generator loss: 0.7932
Epoch 0/1... Batch 2080/6331... Discriminator loss: 1.4017 Generator loss: 0.7528
Epoch 0/1... Batch 2090/6331... Discriminator loss: 1.4527 Generator loss: 0.7330
Epoch 0/1... Batch 2100/6331... Discriminator loss: 1.4339 Generator loss: 0.7691
Batches:  33%|███████████████████████▉                                                | 2100/6331 [04:59<09:37,  7.32batch/s]
Epoch 0/1... Batch 2110/6331... Discriminator loss: 1.3758 Generator loss: 0.7486
Epoch 0/1... Batch 2120/6331... Discriminator loss: 1.4308 Generator loss: 0.7085
Epoch 0/1... Batch 2130/6331... Discriminator loss: 1.4219 Generator loss: 0.7218
Epoch 0/1... Batch 2140/6331... Discriminator loss: 1.3756 Generator loss: 0.7116
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Epoch 0/1... Batch 2160/6331... Discriminator loss: 1.4009 Generator loss: 0.8015
Epoch 0/1... Batch 2170/6331... Discriminator loss: 1.4344 Generator loss: 0.6958
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Epoch 0/1... Batch 2200/6331... Discriminator loss: 1.4273 Generator loss: 0.7899
Batches:  35%|█████████████████████████                                               | 2200/6331 [05:13<09:28,  7.27batch/s]
Epoch 0/1... Batch 2210/6331... Discriminator loss: 1.4854 Generator loss: 0.7197
Epoch 0/1... Batch 2220/6331... Discriminator loss: 1.3913 Generator loss: 0.7291
Epoch 0/1... Batch 2230/6331... Discriminator loss: 1.3755 Generator loss: 0.7964
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Epoch 0/1... Batch 2270/6331... Discriminator loss: 1.4181 Generator loss: 0.7979
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Epoch 0/1... Batch 2300/6331... Discriminator loss: 1.4114 Generator loss: 0.6959
Batches:  36%|██████████████████████████▏                                             | 2300/6331 [05:27<09:15,  7.26batch/s]
Epoch 0/1... Batch 2310/6331... Discriminator loss: 1.4375 Generator loss: 0.7839
Epoch 0/1... Batch 2320/6331... Discriminator loss: 1.3838 Generator loss: 0.8147
Epoch 0/1... Batch 2330/6331... Discriminator loss: 1.4355 Generator loss: 0.7948
Epoch 0/1... Batch 2340/6331... Discriminator loss: 1.4107 Generator loss: 0.7615
Epoch 0/1... Batch 2350/6331... Discriminator loss: 1.4661 Generator loss: 0.5680
Epoch 0/1... Batch 2360/6331... Discriminator loss: 1.3978 Generator loss: 0.7313
Epoch 0/1... Batch 2370/6331... Discriminator loss: 1.5036 Generator loss: 0.6057
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Batches:  38%|███████████████████████████▎                                            | 2400/6331 [05:41<08:59,  7.29batch/s]
Epoch 0/1... Batch 2410/6331... Discriminator loss: 1.3933 Generator loss: 0.8716
Epoch 0/1... Batch 2420/6331... Discriminator loss: 1.4074 Generator loss: 0.8618
Epoch 0/1... Batch 2430/6331... Discriminator loss: 1.3782 Generator loss: 0.7581
Epoch 0/1... Batch 2440/6331... Discriminator loss: 1.3948 Generator loss: 0.7745
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Epoch 0/1... Batch 2470/6331... Discriminator loss: 1.4096 Generator loss: 0.7100
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Batches:  39%|████████████████████████████▍                                           | 2500/6331 [05:54<08:41,  7.35batch/s]
Epoch 0/1... Batch 2510/6331... Discriminator loss: 1.4181 Generator loss: 0.7746
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Batches:  41%|█████████████████████████████▌                                          | 2600/6331 [06:09<08:37,  7.21batch/s]
Epoch 0/1... Batch 2610/6331... Discriminator loss: 1.4188 Generator loss: 0.7466
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Epoch 0/1... Batch 2660/6331... Discriminator loss: 1.3808 Generator loss: 0.8834
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Batches:  43%|██████████████████████████████▋                                         | 2700/6331 [06:23<08:32,  7.08batch/s]
Epoch 0/1... Batch 2710/6331... Discriminator loss: 1.3650 Generator loss: 0.7843
Epoch 0/1... Batch 2720/6331... Discriminator loss: 1.4168 Generator loss: 0.7429
Epoch 0/1... Batch 2730/6331... Discriminator loss: 1.4110 Generator loss: 0.8133
Epoch 0/1... Batch 2740/6331... Discriminator loss: 1.4019 Generator loss: 0.7155
Epoch 0/1... Batch 2750/6331... Discriminator loss: 1.4140 Generator loss: 0.8374
Epoch 0/1... Batch 2760/6331... Discriminator loss: 1.3974 Generator loss: 0.8085
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Epoch 0/1... Batch 2780/6331... Discriminator loss: 1.3944 Generator loss: 0.7467
Epoch 0/1... Batch 2790/6331... Discriminator loss: 1.4266 Generator loss: 0.7280
Epoch 0/1... Batch 2800/6331... Discriminator loss: 1.4359 Generator loss: 0.6858
Batches:  44%|███████████████████████████████▊                                        | 2800/6331 [06:38<08:24,  7.00batch/s]
Epoch 0/1... Batch 2810/6331... Discriminator loss: 1.4575 Generator loss: 0.7500
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Batches:  46%|████████████████████████████████▉                                       | 2900/6331 [06:51<08:02,  7.11batch/s]
Epoch 0/1... Batch 2910/6331... Discriminator loss: 1.4024 Generator loss: 0.7592
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Batches:  47%|██████████████████████████████████                                      | 3000/6331 [07:05<07:45,  7.16batch/s]
Epoch 0/1... Batch 3010/6331... Discriminator loss: 1.3930 Generator loss: 0.8224
Epoch 0/1... Batch 3020/6331... Discriminator loss: 1.3934 Generator loss: 0.7287
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Batches:  49%|███████████████████████████████████▎                                    | 3100/6331 [07:19<07:33,  7.13batch/s]
Epoch 0/1... Batch 3110/6331... Discriminator loss: 1.3852 Generator loss: 0.8031
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Batches:  51%|████████████████████████████████████▍                                   | 3200/6331 [07:34<07:26,  7.01batch/s]
Epoch 0/1... Batch 3210/6331... Discriminator loss: 1.4309 Generator loss: 0.7261
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Batches:  52%|█████████████████████████████████████▌                                  | 3300/6331 [07:48<07:09,  7.06batch/s]
Epoch 0/1... Batch 3310/6331... Discriminator loss: 1.4007 Generator loss: 0.7903
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Batches:  54%|██████████████████████████████████████▋                                 | 3400/6331 [08:02<06:53,  7.08batch/s]
Epoch 0/1... Batch 3410/6331... Discriminator loss: 1.3905 Generator loss: 0.7360
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Batches:  55%|███████████████████████████████████████▊                                | 3500/6331 [08:17<06:43,  7.01batch/s]
Epoch 0/1... Batch 3510/6331... Discriminator loss: 1.3694 Generator loss: 0.7741
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Batches:  57%|████████████████████████████████████████▉                               | 3600/6331 [08:34<06:50,  6.65batch/s]
Epoch 0/1... Batch 3610/6331... Discriminator loss: 1.4018 Generator loss: 0.7677
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Epoch 0/1... Batch 3710/6331... Discriminator loss: 1.3890 Generator loss: 0.7652
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Epoch 0/1... Batch 5830/6331... Discriminator loss: 1.3820 Generator loss: 0.7841
Epoch 0/1... Batch 5840/6331... Discriminator loss: 1.3955 Generator loss: 0.8000
Epoch 0/1... Batch 5850/6331... Discriminator loss: 1.3757 Generator loss: 0.7693
Epoch 0/1... Batch 5860/6331... Discriminator loss: 1.3777 Generator loss: 0.7946
Epoch 0/1... Batch 5870/6331... Discriminator loss: 1.3788 Generator loss: 0.8241
Epoch 0/1... Batch 5880/6331... Discriminator loss: 1.3861 Generator loss: 0.7736
Epoch 0/1... Batch 5890/6331... Discriminator loss: 1.3886 Generator loss: 0.8349
Epoch 0/1... Batch 5900/6331... Discriminator loss: 1.3784 Generator loss: 0.8161
Batches:  93%|███████████████████████████████████████████████████████████████████     | 5900/6331 [14:01<00:59,  7.22batch/s]
Epoch 0/1... Batch 5910/6331... Discriminator loss: 1.3886 Generator loss: 0.7604
Epoch 0/1... Batch 5920/6331... Discriminator loss: 1.3908 Generator loss: 0.7743
Epoch 0/1... Batch 5930/6331... Discriminator loss: 1.3764 Generator loss: 0.8068
Epoch 0/1... Batch 5940/6331... Discriminator loss: 1.3834 Generator loss: 0.8251
Epoch 0/1... Batch 5950/6331... Discriminator loss: 1.3810 Generator loss: 0.7784
Epoch 0/1... Batch 5960/6331... Discriminator loss: 1.3804 Generator loss: 0.8115
Epoch 0/1... Batch 5970/6331... Discriminator loss: 1.3803 Generator loss: 0.8105
Epoch 0/1... Batch 5980/6331... Discriminator loss: 1.3814 Generator loss: 0.8038
Epoch 0/1... Batch 5990/6331... Discriminator loss: 1.3864 Generator loss: 0.7633
Epoch 0/1... Batch 6000/6331... Discriminator loss: 1.3810 Generator loss: 0.7853
Batches:  95%|████████████████████████████████████████████████████████████████████▏   | 6000/6331 [14:16<00:46,  7.07batch/s]
Epoch 0/1... Batch 6010/6331... Discriminator loss: 1.3822 Generator loss: 0.7621
Epoch 0/1... Batch 6020/6331... Discriminator loss: 1.3783 Generator loss: 0.7884
Epoch 0/1... Batch 6030/6331... Discriminator loss: 1.3806 Generator loss: 0.8105
Epoch 0/1... Batch 6040/6331... Discriminator loss: 1.3842 Generator loss: 0.7888
Epoch 0/1... Batch 6050/6331... Discriminator loss: 1.3902 Generator loss: 0.7795
Epoch 0/1... Batch 6060/6331... Discriminator loss: 1.3846 Generator loss: 0.7342
Epoch 0/1... Batch 6070/6331... Discriminator loss: 1.3891 Generator loss: 0.7934
Epoch 0/1... Batch 6080/6331... Discriminator loss: 1.3813 Generator loss: 0.7971
Epoch 0/1... Batch 6090/6331... Discriminator loss: 1.3814 Generator loss: 0.8024
Epoch 0/1... Batch 6100/6331... Discriminator loss: 1.3859 Generator loss: 0.7973
Batches:  96%|█████████████████████████████████████████████████████████████████████▎  | 6100/6331 [14:30<00:32,  7.09batch/s]
Epoch 0/1... Batch 6110/6331... Discriminator loss: 1.3799 Generator loss: 0.7836
Epoch 0/1... Batch 6120/6331... Discriminator loss: 1.3919 Generator loss: 0.8200
Epoch 0/1... Batch 6130/6331... Discriminator loss: 1.3857 Generator loss: 0.7847
Epoch 0/1... Batch 6140/6331... Discriminator loss: 1.3788 Generator loss: 0.7935
Epoch 0/1... Batch 6150/6331... Discriminator loss: 1.3809 Generator loss: 0.7719
Epoch 0/1... Batch 6160/6331... Discriminator loss: 1.3923 Generator loss: 0.7756
Epoch 0/1... Batch 6170/6331... Discriminator loss: 1.3854 Generator loss: 0.7348
Epoch 0/1... Batch 6180/6331... Discriminator loss: 1.3812 Generator loss: 0.8104
Epoch 0/1... Batch 6190/6331... Discriminator loss: 1.3834 Generator loss: 0.7891
Epoch 0/1... Batch 6200/6331... Discriminator loss: 1.3778 Generator loss: 0.8395
Batches:  98%|██████████████████████████████████████████████████████████████████████▌ | 6200/6331 [14:45<00:18,  7.06batch/s]
Epoch 0/1... Batch 6210/6331... Discriminator loss: 1.3830 Generator loss: 0.7872
Epoch 0/1... Batch 6220/6331... Discriminator loss: 1.3848 Generator loss: 0.7984
Epoch 0/1... Batch 6230/6331... Discriminator loss: 1.3938 Generator loss: 0.7494
Epoch 0/1... Batch 6240/6331... Discriminator loss: 1.3821 Generator loss: 0.8132
Epoch 0/1... Batch 6250/6331... Discriminator loss: 1.3824 Generator loss: 0.7746
Epoch 0/1... Batch 6260/6331... Discriminator loss: 1.3856 Generator loss: 0.7712
Epoch 0/1... Batch 6270/6331... Discriminator loss: 1.3856 Generator loss: 0.8516
Epoch 0/1... Batch 6280/6331... Discriminator loss: 1.3829 Generator loss: 0.8427
Epoch 0/1... Batch 6290/6331... Discriminator loss: 1.3771 Generator loss: 0.8055
Epoch 0/1... Batch 6300/6331... Discriminator loss: 1.3795 Generator loss: 0.7853
Batches: 100%|███████████████████████████████████████████████████████████████████████▋| 6300/6331 [15:00<00:04,  6.91batch/s]
Epoch 0/1... Batch 6310/6331... Discriminator loss: 1.3818 Generator loss: 0.7901
Epoch 0/1... Batch 6320/6331... Discriminator loss: 1.3860 Generator loss: 0.7909
Epoch 0/1... Batch 6330/6331... Discriminator loss: 1.3785 Generator loss: 0.7834
Epochs: 100%|██████████████████████████████████████████████████████████████████████████████| 1/1 [15:04<00:00, 904.21s/epoch]
In [ ]:
batch_size = 32
z_dim = 200
learning_rate = 0.01 # too high
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [ ]:
batch_size = 32
z_dim = 200
learning_rate = 0.004 # too high
beta1 = 0.5
alpha = 0.2

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode, alpha)
In [18]:
z_dim = 200
beta1 = 0.5
In [41]:
# Generate some random faces
def generate_faces(checkpoint=None, z_dim=z_dim, beta1=beta1, show_n_images=25):
    graph = tf.Graph()
    if not checkpoint:
        checkpoint = tf.train.latest_checkpoint('checkpoints')
    print('Generating faces from saved checkpoint:', checkpoint)
    with graph.as_default():
        input_real, input_z, learn_rate = model_inputs(28, 28, 3, z_dim)
        d_loss, g_loss = model_loss(input_real, input_z, 3)
        d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
        #dummy = tf.Variable(0)
        saver = tf.train.Saver()
        with tf.Session() as sess:
            #sess.run(tf.global_variables_initializer())
            saver.restore(sess, checkpoint)
            show_generator_output(sess, show_n_images, input_z, 3, 'RGB')
In [68]:
generate_faces()
Generating faces from saved checkpoint: checkpoints\generator_bs32_zd200_lr0.002_b0.3.ckpt
In [71]:
generate_faces(show_n_images=16)
Generating faces from saved checkpoint: checkpoints\generator_bs32_zd200_lr0.002_b0.3.ckpt

So far the hyperparameters that give better looking visuals

batch_size = 32
z_dim = 200
learning_rate = 0.002
beta1 = 0.5
alpha = 0.2
In [36]:
generate_faces('checkpoints\generator_bs32_zd200_lr0.002_b0.5.ckpt')
Generating faces from saved checkpoint: checkpoints\generator_bs32_zd200_lr0.002_b0.5.ckpt
In [76]:
generate_faces('checkpoints\generator_bs32_zd200_lr0.002_b0.5.ckpt', show_n_images=16)
Generating faces from saved checkpoint: checkpoints\generator_bs32_zd200_lr0.002_b0.5.ckpt

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.